diff --git a/.gitignore b/.gitignore
index c6ec397..38f8524 100644
--- a/.gitignore
+++ b/.gitignore
@@ -19,4 +19,7 @@ wheels/
 .mypy_cache/
 
 # VS Code
-.vscode/
\ No newline at end of file
+.vscode/
+
+# MkDocs
+site/
\ No newline at end of file
diff --git a/.ruff.toml b/.ruff.toml
index 767720a..765d37d 100644
--- a/.ruff.toml
+++ b/.ruff.toml
@@ -1,6 +1,6 @@
 fix = true
 line-length = 88
-target-version = "py311"
+target-version = "py312"
 
 [format]
 docstring-code-format = true # Enable reformatting of code snippets in docstrings.
diff --git a/docs/reference/masks/mask_builders.md b/docs/reference/masks/mask_builders.md
new file mode 100644
index 0000000..bf89ce6
--- /dev/null
+++ b/docs/reference/masks/mask_builders.md
@@ -0,0 +1,181 @@
+# Mask Builders
+
+Mask builders are tools for assembling feature masks from neural network predictions or other tile-level data. They handle the complexity of combining overlapping tiles, scaling between coordinate spaces, and managing memory for large output masks using a flexible strategy-based architecture.
+
+## Overview
+
+When processing whole-slide images with neural networks, you often need to:
+
+1. Extract tiles from a slide
+2. Run inference to get predictions or features for each tile
+3. Assemble these predictions back into a full-resolution mask
+
+Mask builders automate step 3, handling:
+
+- **Coordinate scaling**: Converting from source WSI coordinates to mask coordinates — including automatic GCD-based compression when tiles and strides share common factors.
+- **Overlap handling**: Averaging or taking the maximum when tiles overlap.
+- **Memory management**: Using in-memory arrays or memory-mapped files for large masks.
+- **Scalar expansion**: Broadcasting scalar per-tile predictions `(B, C)` into spatial tiles automatically.
+- **Edge clipping**: Removing border artifacts from model output tiles at update time.
+
+## MaskBuilder
+
+::: ratiopath.masks.mask_builders.MaskBuilder
+
+The `MaskBuilder` is the central orchestrator. You configure it by providing:
+
+- `source_extents`: Spatial dimensions of the source WSI (H, W, ...).
+- `source_tile_extent`: Spatial dimensions of the model input tiles.
+- `output_tile_extent`: Spatial dimensions of the model output tiles (can differ from input due to pooling/stride).
+- `stride`: Stride between tiles in source resolution.
+- `storage`: Where the mask is stored — `"inmemory"` (RAM) or `"memmap"` (disk-backed).
+- `aggregation`: How overlapping tiles are merged — `MeanAggregator` (default) or `MaxAggregator`.
+
+The mask shape is computed automatically from the source extents, tile extents, and stride using GCD-based compression for efficient memory use.
+
+## Components
+
+### Storage Strategies
+
+::: ratiopath.masks.mask_builders.InMemory
+::: ratiopath.masks.mask_builders.MemMap
+
+### Aggregation Strategies
+
+::: ratiopath.masks.mask_builders.MeanAggregator
+::: ratiopath.masks.mask_builders.MaxAggregator
+
+## Examples
+
+### Averaging Scalar Predictions
+
+**Use case**: You have scalar predictions (e.g., class probabilities) for each tile. Each prediction is uniformly expanded to fill the tile's footprint, and overlapping regions are averaged.
+
+```python
+import numpy as np
+import openslide
+from ratiopath.masks.mask_builders import MaskBuilder, MeanAggregator
+import matplotlib.pyplot as plt
+
+# Set up tiling parameters
+LEVEL = 3
+tile_extents = (512, 512)
+tile_strides = (256, 256)
+slide = openslide.OpenSlide("path/to/slide.mrxs")
+slide_w, slide_h = slide.level_dimensions[LEVEL]
+
+# output_tile_extent=(1, 1) means scalar data — the builder
+# broadcasts (B, C) → (B, C, 1, 1) and upscales automatically.
+mask_builder = MaskBuilder(
+    source_extents=(slide_h, slide_w),
+    source_tile_extent=tile_extents,
+    output_tile_extent=(1, 1),
+    stride=tile_strides,
+    n_channels=1,
+    storage="inmemory",
+    aggregation=MeanAggregator,
+    dtype=np.float32,
+)
+
+# Process tiles
+for tiles, xs, ys in generate_tiles_from_slide(slide, LEVEL, tile_extents, tile_strides):
+    features = model.predict(tiles)  # features shape: (B, 1)
+    coords_batch = np.stack([ys, xs], axis=1)  # shape: (B, 2)
+    mask_builder.update_batch(features, coords_batch)
+
+# Finalize — MeanAggregator returns {"mask": ..., "overlap_counter": ...}
+results = mask_builder.finalize()
+assembled_mask = results["mask"]
+overlap_counter = results["overlap_counter"]
+
+plt.imshow(assembled_mask[0], cmap="gray")
+plt.show()
+
+# Always clean up to release storage resources
+mask_builder.cleanup()
+```
+
+---
+
+### Max Aggregation with Edge Clipping (MemMap)
+
+**Use case**: You have high-resolution feature maps. You want to preserve the maximum signal where tiles overlap, remove border pixels from each tile edge to avoid artifacts, and use disk storage because the mask is very large.
+
+```python
+import numpy as np
+from ratiopath.masks.mask_builders import MaskBuilder, MaxAggregator
+
+# Dense output — output tiles match input tiles in spatial size
+mask_builder = MaskBuilder(
+    source_extents=(10000, 10000),
+    source_tile_extent=(512, 512),
+    output_tile_extent=(512, 512),
+    stride=(256, 256),
+    n_channels=3,
+    storage="memmap",
+    aggregation=MaxAggregator,
+    dtype=np.float32,
+    filename="large_mask.npy",  # persisted to disk
+)
+
+for tiles, coords in tile_generator:
+    predictions = model.predict(tiles)  # (B, 3, 512, 512)
+    # edge_clipping=4 removes 4px from each edge of every tile
+    mask_builder.update_batch(predictions, coords, edge_clipping=4)
+
+# MaxAggregator returns the accumulator NDArray directly
+assembled_mask = mask_builder.finalize()
+mask_builder.cleanup()
+```
+
+---
+
+### Auto-Scaling Coordinates (Different Input/Output Resolution)
+
+**Use case**: Your model's output tiles have different spatial dimensions than the input tiles (e.g., due to stride or pooling). The builder auto-scales coordinates between source and mask resolution.
+
+```python
+import numpy as np
+from ratiopath.masks.mask_builders import MaskBuilder, MeanAggregator
+
+# Model takes 512×512 input tiles, produces 128×128 output tiles (4× downsampled)
+mask_builder = MaskBuilder(
+    source_extents=(2000, 2000),
+    source_tile_extent=(512, 512),
+    output_tile_extent=(128, 128),
+    stride=(256, 256),
+    n_channels=1,
+    storage="inmemory",
+    aggregation=MeanAggregator,
+    dtype=np.float32,
+)
+
+# Coordinates are always in SOURCE resolution — the builder
+# handles the conversion to mask resolution internally.
+for tiles, coords in tile_generator:
+    predictions = model.predict(tiles)  # (B, 1, 128, 128)
+    mask_builder.update_batch(predictions, coords)
+
+results = mask_builder.finalize()
+mask_builder.cleanup()
+```
+
+## Coordinate System Notes
+
+All mask builders expect coordinates in the format `(B, N)` where:
+
+- `B` is the batch size.
+- `N` is the number of spatial dimensions (typically 2 for height and width).
+
+Note the order: `[ys, xs]` not `[xs, ys]`, as the first dimension represents height (y) and the second represents width (x), matching the NumPy `(C, H, W)` convention used by the builder.
+
+## Lifecycle
+
+Always call `cleanup()` when you are done with a `MaskBuilder` to release storage resources (especially important for `MemMap` storage which holds file handles):
+
+```python
+mask_builder = MaskBuilder(...)
+# ... update_batch calls ...
+results = mask_builder.finalize()
+mask_builder.cleanup()
+```
diff --git a/mkdocs.yml b/mkdocs.yml
index 162b66f..4474ea3 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -19,9 +19,12 @@ nav:
           - read_slide_tile: reference/tiling/read_slide_tile.md
           - tilers: reference/tiling/tilers.md
           - utils: reference/tiling/utils.md
+      - Masks:
+          - mask_builders: reference/masks/mask_builders.md
       - Parsers:
           - ASAPParser: reference/parsers/asap.md
           - GeoJSONParser: reference/parsers/geojson.md
+          - Darwin7JSONParser: reference/parsers/darwin.md
       - Augmentations:
           - estimate_stain_vectors: reference/augmentations/estimate_stain_vectors.md
           - StainAugmentor: reference/augmentations/stain_augmentor.md
diff --git a/pyproject.toml b/pyproject.toml
index 4b6e514..5a50858 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "ratiopath"
-version = "1.3.1"
+version = "1.4.0"
 description = "A library for efficient processing and analysis of whole-slide pathology images."
 authors = [
     { name = "Matěj Pekár", email = "matejpekar@mail.muni.cz" },
diff --git a/ratiopath/masks/__init__.py b/ratiopath/masks/__init__.py
index a26d695..aebd523 100644
--- a/ratiopath/masks/__init__.py
+++ b/ratiopath/masks/__init__.py
@@ -1,4 +1,5 @@
 from ratiopath.masks.tissue_mask import tissue_mask
+from ratiopath.masks.write_big_tiff import write_big_tiff
 
 
-__all__ = ["tissue_mask"]
+__all__ = ["tissue_mask", "write_big_tiff"]
diff --git a/ratiopath/masks/mask_builders/__init__.py b/ratiopath/masks/mask_builders/__init__.py
new file mode 100644
index 0000000..1b9a87a
--- /dev/null
+++ b/ratiopath/masks/mask_builders/__init__.py
@@ -0,0 +1,17 @@
+from ratiopath.masks.mask_builders.aggregation import (
+    Aggregator,
+    MaxAggregator,
+    MeanAggregator,
+)
+from ratiopath.masks.mask_builders.mask_builder import MaskBuilder
+from ratiopath.masks.mask_builders.storage import InMemory, MemMap
+
+
+__all__ = [
+    "Aggregator",
+    "InMemory",
+    "MaskBuilder",
+    "MaxAggregator",
+    "MeanAggregator",
+    "MemMap",
+]
diff --git a/ratiopath/masks/mask_builders/aggregation.py b/ratiopath/masks/mask_builders/aggregation.py
new file mode 100644
index 0000000..15ef85c
--- /dev/null
+++ b/ratiopath/masks/mask_builders/aggregation.py
@@ -0,0 +1,136 @@
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from pathlib import Path
+from typing import TYPE_CHECKING, Any, TypedDict, cast
+
+import numpy as np
+from numpy.typing import NDArray
+
+
+if TYPE_CHECKING:
+    from collections.abc import Callable
+
+
+class Aggregator[DType: np.generic, R](ABC):
+    """Abstract base class for aggregation strategies."""
+
+    def __init__(self, storage: NDArray[DType], **kwargs: Any) -> None:
+        return
+
+    @abstractmethod
+    def update(
+        self, accumulator: NDArray[DType], sample: np.ndarray, coords: NDArray[np.int64]
+    ) -> None:
+        """Update the accumulator with a single tile sample."""
+
+    @abstractmethod
+    def finalize(self, accumulator: NDArray[DType]) -> R:
+        """Finalize the mask assembly and return the result."""
+
+    def cleanup(self) -> None:
+        """Optional cleanup method to release resources if needed."""
+        return
+
+    def _get_acc_slices(
+        self, coords: NDArray[np.int64], mask_tile_extents: NDArray[np.int64]
+    ) -> tuple[slice, ...]:
+        """Compute slice objects for accumulator indexing.
+
+        Args:
+            coords: Array of shape (N,) with top-left coordinates in N dimensions.
+            mask_tile_extents: Array of shape (N,) with tile size in mask space for each dimension.
+
+        Returns:
+            Tuple containing N slice objects for indexing into the accumulator.
+        """
+        tile_end_coords = coords + mask_tile_extents
+        return tuple(
+            slice(int(start), int(end))
+            for start, end in zip(coords, tile_end_coords, strict=True)
+        )
+
+
+class MeanAggregatorResults[Dtype: np.generic](TypedDict):
+    mask: NDArray[Dtype]
+    overlap_counter: NDArray[np.uint16]
+
+
+class MeanAggregator[DType: np.generic](
+    Aggregator[DType, MeanAggregatorResults[DType]]
+):
+    """Aggregator that implements averaging aggregation for overlapping tiles.
+
+    This aggregator accumulates tiles by addition and tracks the overlap count at each pixel.
+    During finalization, the accumulated values are divided by the overlap count to compute
+    the average value at each position. This is useful for:
+    - Smoothly blending overlapping tile predictions
+    - Reducing edge artifacts in sliding window processing
+    - Computing ensemble averages from multiple passes
+
+    The aggregator allocates an additional `overlap_counter` accumulator with shape (1, *SpatialDims)
+    to track how many tiles contributed to each pixel position.
+    """
+
+    def __init__(
+        self,
+        storage: NDArray[DType],
+        filename: Path | str | None = None,
+        overlap_counter_filename: Path | str | None = None,
+        **kwargs: Any,
+    ) -> None:
+        overlap_filename = overlap_counter_filename
+        if overlap_filename is None and filename is not None:
+            path = Path(filename)
+            overlap_filename = path.with_suffix(f".overlaps{path.suffix}")
+
+        storage_cls = cast("Callable[..., NDArray[np.uint16]]", type(storage))
+        self.overlap_counter = storage_cls(
+            filename=overlap_filename,
+            shape=(1, *storage.shape[1:]),
+            dtype=np.uint16,
+            **kwargs,
+        )
+
+    def update(
+        self, accumulator: NDArray[DType], sample: np.ndarray, coords: NDArray[np.int64]
+    ) -> None:
+        mask_tile_extents = np.asarray(sample.shape[1:], dtype=np.int64)
+        acc_slices = self._get_acc_slices(coords, mask_tile_extents)
+        accumulator[:, *acc_slices] += sample  # type: ignore[misc]
+        self.overlap_counter[:, *acc_slices] += 1
+
+    def finalize(self, accumulator: NDArray[DType]) -> MeanAggregatorResults[DType]:
+        accumulator /= self.overlap_counter.clip(min=1)  # type: ignore[misc]
+        return {
+            "mask": accumulator,
+            "overlap_counter": self.overlap_counter,
+        }
+
+    def cleanup(self) -> None:
+        if hasattr(self, "overlap_counter"):
+            if hasattr(self.overlap_counter, "close"):
+                self.overlap_counter.close()
+            del self.overlap_counter
+
+
+class MaxAggregator[DType: np.generic](Aggregator[DType, NDArray[DType]]):
+    """Aggregator that implements maximum aggregation for overlapping tiles.
+
+    This aggregator keeps only the maximum value at each pixel position when tiles overlap.
+    No additional storage is required, and finalization is a no-op since the accumulator
+    already contains the final max values. This is useful for:
+    - Maximum intensity projection
+    - Keeping the highest confidence prediction across overlapping tiles
+    - Peak detection across multiple scales
+    """
+
+    def update(
+        self, accumulator: NDArray[DType], sample: np.ndarray, coords: NDArray[np.int64]
+    ) -> None:
+        mask_tile_extents = np.asarray(sample.shape[1:], dtype=np.int64)
+        acc_slices = self._get_acc_slices(coords, mask_tile_extents)
+        accumulator[:, *acc_slices] = np.maximum(accumulator[:, *acc_slices], sample)
+
+    def finalize(self, accumulator: NDArray[DType]) -> NDArray[DType]:
+        return accumulator
diff --git a/ratiopath/masks/mask_builders/mask_builder.py b/ratiopath/masks/mask_builders/mask_builder.py
new file mode 100644
index 0000000..6ee1683
--- /dev/null
+++ b/ratiopath/masks/mask_builders/mask_builder.py
@@ -0,0 +1,231 @@
+from __future__ import annotations
+
+from functools import lru_cache
+from typing import TYPE_CHECKING, Any, Literal
+
+import numpy as np
+from numpy.typing import NDArray
+
+from ratiopath.masks.mask_builders.aggregation import (
+    Aggregator,
+    MeanAggregator,
+)
+
+
+if TYPE_CHECKING:
+    from collections.abc import Callable
+
+    import pyvips
+
+
+type EdgeClipping = int | tuple[int, ...] | tuple[tuple[int, int], ...]
+
+
+@lru_cache(maxsize=32)
+def _prepare_clipping(
+    num_spatial_dims: int,
+    tile_spatial_shape: tuple[int, ...],
+    edge_clipping: EdgeClipping,
+) -> tuple[tuple[slice, ...], NDArray[np.int64]]:
+    """Calculate full slice objects and coordinate shifts for edge clipping."""
+    if isinstance(edge_clipping, int):
+        clip_start = np.full(num_spatial_dims, edge_clipping, dtype=np.int64)
+        clip_end = np.full(num_spatial_dims, edge_clipping, dtype=np.int64)
+    elif (
+        isinstance(edge_clipping, tuple)
+        and len(edge_clipping) == num_spatial_dims
+        and all(
+            isinstance(clip, tuple)
+            and len(clip) == 2
+            and all(isinstance(value, int) for value in clip)
+            for clip in edge_clipping
+        )
+    ):
+        clip_pairs = np.asarray(edge_clipping, dtype=np.int64)
+        clip_start = clip_pairs[:, 0]
+        clip_end = clip_pairs[:, 1]
+    elif isinstance(edge_clipping, tuple) and len(edge_clipping) == num_spatial_dims:
+        clip_start = np.asarray(edge_clipping, dtype=np.int64)
+        clip_end = np.asarray(edge_clipping, dtype=np.int64)
+    else:
+        raise ValueError(
+            "edge_clipping must be an int, "
+            f"a tuple of {num_spatial_dims} ints, "
+            f"or a tuple of {num_spatial_dims} (start, end) pairs."
+        )
+
+    slices = (
+        slice(None),
+        slice(None),
+        *(
+            slice(start, extent - end)
+            for start, end, extent in zip(
+                clip_start, clip_end, tile_spatial_shape, strict=True
+            )
+        ),
+    )
+    return slices, clip_start
+
+
+class MaskBuilder[DType: np.generic, AggregatorR]:
+    """Builder for assembling large masks from tiled data with automatic scaling and clipping.
+
+    This class coordinates:
+    - Coordinate scaling: Maps source WSI coordinates to mask resolution.
+    - Edge clipping: Removes artifacts from model output tiles.
+    - Pluggable storage: Allocates memory (RAM vs disk-backed).
+    - Pluggable aggregation: Merges overlapping tiles (e.g., mean, max).
+    """
+
+    storage: NDArray[DType]
+    aggregator: Aggregator[DType, AggregatorR]
+
+    def __init__(
+        self,
+        source_extents: tuple[int, ...],
+        source_tile_extent: int | tuple[int, ...],
+        output_tile_extent: int | tuple[int, ...],
+        stride: int | tuple[int, ...],
+        n_channels: int = 1,
+        storage: Literal["inmemory", "memmap"]
+        | Callable[..., NDArray[DType]] = "inmemory",
+        aggregation: type[Aggregator[DType, AggregatorR]] = MeanAggregator,  # type: ignore[assignment]
+        dtype: type[DType] = np.float32,  # type: ignore[assignment]
+        **kwargs: Any,
+    ) -> None:
+        """Initialize the mask builder.
+
+        Args:
+            source_extents: Spatial dimensions (H, W, ...) of the source WSI.
+            source_tile_extent: Spatial dimensions of the model input tiles.
+            output_tile_extent: Spatial dimensions of the model output tiles.
+            stride: Stride between tiles in source resolution.
+            n_channels: Number of channels in the output mask.
+            storage: Strategy for allocating memory ("inmemory", "memmap", or a class).
+            aggregation: Strategy for combining tiles ("mean", "max", or a class).
+            dtype: Data type for the accumulator.
+            kwargs: Extra arguments passed to storage and aggregation initialization.
+        """
+        # Normalize spatial dimensions to arrays
+        self.source_extents = np.asarray(source_extents, dtype=np.int64)
+        self.source_tile_extent = np.broadcast_to(
+            source_tile_extent, len(source_extents)
+        )
+        self.output_tile_extent = np.broadcast_to(
+            output_tile_extent, len(source_extents)
+        )
+        self.stride = np.broadcast_to(stride, len(source_extents))
+
+        # Calculate how many tiles are required to fully cover the WSI (implicitly padding the edges)
+        # Using np.ceil to ensure the right/bottom edges are fully covered.
+        num_tiles = (
+            np.ceil(
+                np.maximum(0, self.source_extents - self.source_tile_extent)
+                / self.stride
+            ).astype(np.int64)
+            + 1
+        )
+
+        # Calculate the actual spatial span these tiles cover in the original WSI space
+        self.span = (num_tiles - 1) * self.stride + self.source_tile_extent
+
+        # Find the Greatest Common Divisor for the alignment math
+        gcd = np.gcd(self.source_tile_extent, self.stride * self.output_tile_extent)
+
+        # Calculate the required MaskBuilder properties
+        self.mask_extents = (self.span * self.output_tile_extent) // gcd
+        self.upscale_factor = self.source_tile_extent // gcd
+        self.mask_stride = (self.stride * self.output_tile_extent) // gcd
+
+        # Initialize Storage
+        if isinstance(storage, str):
+            if storage == "inmemory":
+                from ratiopath.masks.mask_builders.storage import InMemory
+
+                storage = InMemory
+            elif storage == "memmap":
+                from ratiopath.masks.mask_builders.storage import MemMap
+
+                storage = MemMap
+            else:
+                raise ValueError(f"Unknown storage type: {storage}")
+
+        self.storage = storage(
+            shape=(n_channels, *self.mask_extents), dtype=dtype, **kwargs
+        )
+
+        # Initialize Aggregator
+        self.aggregator = aggregation(storage=self.storage, **kwargs)
+
+    def update_batch(
+        self,
+        batch: NDArray[DType],
+        coords: NDArray[np.int64],
+        edge_clipping: EdgeClipping = 0,
+    ) -> None:
+        """Update the accumulator with a batch of tiles.
+
+        Args:
+            batch: Array of shape (B, C, *SpatialDims) or (B, C) containing B tiles.
+            coords: Array of shape (B, N) containing top-left coordinates in source resolution.
+            edge_clipping: Pixels to clip from tile edges.
+                Supports an int (symmetric for all dims), a tuple of N ints
+                (symmetric per dim), or a tuple of N (start, end) pairs.
+        """
+        # Scale coordinates from source to mask resolution
+        # Find which tile index this corresponds to and multiply by the mask stride
+        # to get the starting pixel in the mask array
+        coords = (coords // self.stride) * self.mask_stride
+
+        # Handle scalar data expansion
+        # Broadcast scalar (B, C) to (B, C, *output_tile_extent)
+        # If already dense, this is a fast view operation
+        num_missing_dims = len(self.mask_extents) + 2 - batch.ndim
+        batch = np.broadcast_to(
+            batch[..., *[np.newaxis] * num_missing_dims],
+            (*batch.shape[:2], *self.output_tile_extent),
+        )
+
+        # Apply Edge Clipping
+        slices, shift = _prepare_clipping(
+            len(self.mask_extents), tuple(self.output_tile_extent), edge_clipping
+        )
+        batch = batch[slices]
+        coords += shift * self.upscale_factor
+
+        # Upscale the batch to match the mask resolution if needed
+        for axis_idx, factor in enumerate(self.upscale_factor, start=2):
+            if factor > 1:
+                batch = np.repeat(batch, factor, axis=axis_idx)
+
+        for sample, coord in zip(batch, coords, strict=True):
+            self.aggregator.update(self.storage, sample, coord)
+
+    def finalize(self) -> AggregatorR:
+        return self.aggregator.finalize(self.storage)
+
+    def resize_to_source(self, image: NDArray[DType]) -> pyvips.Image:
+        """Resize a mask array to the original source resolution using pyvips.
+
+        Args:
+            image: Array of shape (C, H_mask, W_mask) to be resized to (H_source, W_source, C).
+        """
+        import pyvips
+
+        vips_image = pyvips.Image.new_from_array(image.transpose(1, 2, 0))
+
+        scale_factors = self.source_extents / self.mask_extents
+        vips_image = vips_image.resize(scale_factors[1], vscale=scale_factors[0])
+        vips_image = vips_image.crop(
+            0, 0, self.source_extents[1], self.source_extents[0]
+        )
+
+        return vips_image
+
+    def cleanup(self) -> None:
+        if hasattr(self, "storage"):
+            if hasattr(self.storage, "close"):
+                self.storage.close()
+            del self.storage
+
+        self.aggregator.cleanup()
diff --git a/ratiopath/masks/mask_builders/storage.py b/ratiopath/masks/mask_builders/storage.py
new file mode 100644
index 0000000..b452cd5
--- /dev/null
+++ b/ratiopath/masks/mask_builders/storage.py
@@ -0,0 +1,87 @@
+import logging
+import tempfile
+from os import PathLike
+from pathlib import Path
+from typing import Any, Self
+
+import numpy as np
+
+
+logger = logging.getLogger(__name__)
+
+
+class InMemory[DType: np.generic](np.ndarray):
+    """Storage allocator that uses in-memory NumPy arrays."""
+
+    def __new__(cls, shape: tuple[int, ...], dtype: type[DType], **kwargs) -> Self:
+        return np.zeros(shape=shape, dtype=dtype).view(cls)
+
+
+class MemMap[DType: np.generic](np.memmap):
+    """Storage allocator that uses numpy memory-mapped files (memmaps).
+
+    This class provides disk-backed storage for large masks that exceed available RAM.
+    Memory mapping allows the OS to manage paging between disk and memory transparently,
+    enabling processing of masks that would otherwise cause out-of-memory errors.
+
+    **Temporary Files (default behavior when `filename=None`):**
+    A temporary file is created and used as backing storage. The file is deleted when
+    the memmap is closed or garbage collected. Disk space is consumed during processing
+    but automatically reclaimed afterward.
+
+    **Explicit Files (when `filename` is provided):**
+    The memmap is backed by the specified file path, which persists after processing.
+    This is useful for caching results or processing masks too large for temporary storage.
+    If the file already exists, a FileExistsError is raised to prevent accidental data loss.
+
+    This class uses NumPy's NPY format version 3.0 for compatibility with large arrays (>4GB).
+    """
+
+    def __new__(
+        cls,
+        shape: tuple[int, ...],
+        dtype: type[DType],
+        filename: str | PathLike[Any] | None = None,
+        **kwargs,
+    ) -> Self:
+        temp_path = None
+        if filename is None:
+            # delete=False ensures Windows doesn't lock the file prematurely
+            with tempfile.NamedTemporaryFile(delete=False) as file:
+                temp_path = filename = file.name
+        elif Path(filename).exists():
+            raise FileExistsError(f"Memmap {filename} already exists.")
+
+        obj = np.lib.format.open_memmap(
+            filename, mode="w+", shape=shape, dtype=dtype, version=(3, 0)
+        ).view(cls)
+
+        obj._tempfile = temp_path
+        return obj
+
+    def __array_finalize__(self, obj: Any) -> None:
+        """Called automatically when a view or slice of the array is created."""
+        if obj is None:
+            return
+
+        # CRITICAL: If someone slices this array (e.g., subset = my_memmap[:5]),
+        # the slice should NOT own the tempfile. Only the original object should
+        # delete the file during garbage collection.
+        self._tempfile = None
+
+    def close(self) -> None:
+        if hasattr(self, "_mmap") and self._mmap is not None:
+            try:
+                self._mmap.close()
+            except Exception as e:
+                logger.warning(f"Failed to close memmap: {e}")
+
+        if self._tempfile is not None:
+            try:
+                Path(self._tempfile).unlink(missing_ok=True)
+            except Exception as e:
+                logger.warning(f"Failed to delete memmap file {self._tempfile}: {e}")
+            self._tempfile = None
+
+    def __del__(self) -> None:
+        self.close()
diff --git a/ratiopath/masks/tissue_mask.py b/ratiopath/masks/tissue_mask.py
index 46b0c12..18a46e8 100644
--- a/ratiopath/masks/tissue_mask.py
+++ b/ratiopath/masks/tissue_mask.py
@@ -1,29 +1,18 @@
-import pyvips
+from __future__ import annotations
 
-from ratiopath.masks.vips_filters import (
-    VipsClosing,
-    VipsCompose,
-    VipsFilter,
-    VipsGrayScaleFilter,
-    VipsOpening,
-    VipsOtsu,
-)
-from ratiopath.masks.vips_filters.typed import Res
+from typing import TYPE_CHECKING
 
 
-default_filter = VipsCompose(
-    [
-        VipsGrayScaleFilter(),
-        VipsOtsu(),
-        VipsOpening(),
-        VipsClosing(),
-    ]
-)
+if TYPE_CHECKING:
+    import pyvips
+
+    from ratiopath.masks.vips_filters import VipsFilter
+    from ratiopath.masks.vips_filters.typed import Res
 
 
 def tissue_mask(
-    slide: pyvips.Image, mpp: Res, filter: VipsFilter = default_filter
-) -> tuple[pyvips.Image, Res]:
+    slide: pyvips.Image, mpp: Res, filter: VipsFilter | None = None
+) -> pyvips.Image:
     """Generates a tissue mask from a whole-slide image (WSI) using saturation channel extraction and morphological operations, and saves the mask as a TIFF image.
 
     The function extracts the saturation channel from the WSI, applies Otsu thresholding to
@@ -40,4 +29,22 @@ def tissue_mask(
     Returns:
         The generated tissue mask as pyvips.Image.
     """
-    return filter(slide, mpp)
+    from ratiopath.masks.vips_filters import (
+        VipsClosing,
+        VipsCompose,
+        VipsGrayScaleFilter,
+        VipsOpening,
+        VipsOtsu,
+    )
+
+    if filter is None:
+        filter = VipsCompose(
+            [
+                VipsGrayScaleFilter(),
+                VipsOtsu(),
+                VipsClosing(),
+                VipsOpening(),
+            ]
+        )
+
+    return filter(slide, mpp)[0]
diff --git a/ratiopath/masks/vips_filters/typed.py b/ratiopath/masks/vips_filters/typed.py
index 31ae0c5..158c9bb 100644
--- a/ratiopath/masks/vips_filters/typed.py
+++ b/ratiopath/masks/vips_filters/typed.py
@@ -1,4 +1 @@
-from typing import TypeAlias
-
-
-Res: TypeAlias = tuple[float, float]
+type Res = tuple[float, float]
diff --git a/ratiopath/masks/write_big_tiff.py b/ratiopath/masks/write_big_tiff.py
new file mode 100644
index 0000000..fc6f51c
--- /dev/null
+++ b/ratiopath/masks/write_big_tiff.py
@@ -0,0 +1,63 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+
+if TYPE_CHECKING:
+    from pathlib import Path
+
+    import pyvips
+
+
+def write_big_tiff(
+    image: pyvips.Image,
+    path: Path,
+    mpp_x: float,
+    mpp_y: float,
+    tile_width: int = 512,
+    tile_height: int = 512,
+) -> None:
+    """Saves the image as a BigTIFF file using pyvips with predefined settings.
+
+    This function exports an image in BigTIFF format, which is ideal for applications
+    that require multi-resolution pyramid TIFF files, such as xOpat. It leverages
+    [`pyvips.Image.tiffsave`](https://libvips.github.io/pyvips/vimage.html#pyvips.Image.tiffsave)
+    to apply specific parameters for efficient storage and optimal image tiling.
+
+    The image is saved with the following fixed settings:
+        - BigTIFF format enabled (`bigtiff=True`).
+        - DEFLATE compression applied for efficient storage.
+        - Image is tiled with a tile size of tile_width x tile_height.
+        - Multi-level pyramid structure is used (`pyramid=True`), making the image suitable
+            for fast zooming in viewers.
+
+    Args:
+        image: A pyvips Image object representing the input image.
+        path: The file path where the BigTIFF mask will be saved.
+        mpp_x: The horizontal resolution of the image in µm/pixel.
+        mpp_y: The vertical resolution of the image in µm/pixel.
+        tile_width: The width of each tile in pixels. Default is 512.
+            Must match with other masks used in xOpat.
+        tile_height: The height of each tile in pixels. Default is 512.
+            Must match with other masks used in xOpat.
+    """
+    import pyvips
+
+    xres = 1000 / mpp_x
+    yres = 1000 / mpp_y
+
+    # Strip any ICC profile before saving
+    if image.get_typeof("icc-profile-data") != 0:
+        image = image.copy().remove("icc-profile-data")
+
+    image.tiffsave(
+        path,
+        bigtiff=True,
+        compression=pyvips.enums.ForeignTiffCompression.DEFLATE,
+        tile=True,
+        tile_width=tile_width,
+        tile_height=tile_height,
+        xres=xres,
+        yres=yres,
+        pyramid=True,
+    )
diff --git a/ratiopath/model_selection/split.py b/ratiopath/model_selection/split.py
index a304048..f7c23c0 100644
--- a/ratiopath/model_selection/split.py
+++ b/ratiopath/model_selection/split.py
@@ -1,7 +1,7 @@
 import numbers
 from collections.abc import Iterator
 from itertools import chain
-from typing import Any, TypeAlias
+from typing import Any
 
 import numpy as np
 import pandas as pd
@@ -20,10 +20,10 @@
 from sklearn.utils.validation import _num_samples, check_random_state, indexable
 
 
-ArrayLike: TypeAlias = np.typing.ArrayLike
-MatrixLike: TypeAlias = np.ndarray | pd.DataFrame | spmatrix
-Int: TypeAlias = int | np.int8 | np.int16 | np.int32 | np.int64
-Float: TypeAlias = float | np.float16 | np.float32 | np.float64
+type ArrayLike = np.typing.ArrayLike
+type MatrixLike = np.ndarray | pd.DataFrame | spmatrix
+type Int = int | np.int8 | np.int16 | np.int32 | np.int64
+type Float = float | np.float16 | np.float32 | np.float64
 
 
 class StratifiedGroupShuffleSplit(GroupsConsumerMixin, BaseShuffleSplit):
diff --git a/ratiopath/parsers/darwin7_json_parser.py b/ratiopath/parsers/darwin7_json_parser.py
index e6ae6d0..7a73fbc 100644
--- a/ratiopath/parsers/darwin7_json_parser.py
+++ b/ratiopath/parsers/darwin7_json_parser.py
@@ -15,6 +15,7 @@ class Darwin7JSONParser:
     Extracts geometries and metadata from Darwin JSON structures into a GeoDataFrame.
     Supports Polygon, Point, and bounding box geometries.
     Expected JSON schema:
+    ```bash
     Root
     └── annotations (list)
         ├── Feature (Polygon)
@@ -30,6 +31,7 @@ class Darwin7JSONParser:
         └── Feature (Point)
             ├── id: "uuid"
             └── point: {x, y}
+    ```
     """
 
     def __init__(self, file_path: Path | str | TextIO) -> None:
diff --git a/ratiopath/parsers/geojson_parser.py b/ratiopath/parsers/geojson_parser.py
index 1c75ad2..e094bbe 100644
--- a/ratiopath/parsers/geojson_parser.py
+++ b/ratiopath/parsers/geojson_parser.py
@@ -93,6 +93,7 @@ def get_filtered_geodataframe(
                 return self.gdf.iloc[0:0]
 
             series = filtered_gdf[subkeys[0]]
+
             for subkey in subkeys[1:]:
                 series = series.apply(safe_to_dict)
                 mask = series.apply(
diff --git a/ratiopath/tiling/overlays.py b/ratiopath/tiling/overlays.py
index 23bb5c4..1e0dd83 100644
--- a/ratiopath/tiling/overlays.py
+++ b/ratiopath/tiling/overlays.py
@@ -275,4 +275,4 @@ def overlap_fraction(overlay: np.ma.MaskedArray) -> dict[str, float]:
 
     overlap_vectorized = np.vectorize(overlap_fraction, otypes=[object])
 
-    return pa.array(overlap_vectorized(overlay))
+    return pa.array(overlap_vectorized(overlay))  # type: ignore[return-value]
diff --git a/ratiopath/tiling/tilers.py b/ratiopath/tiling/tilers.py
index 7a73d16..6565921 100644
--- a/ratiopath/tiling/tilers.py
+++ b/ratiopath/tiling/tilers.py
@@ -1,16 +1,13 @@
 import itertools
 import warnings
 from collections.abc import Iterator
-from typing import Literal, TypeVarTuple
+from typing import Literal
 
 import numpy as np
 from numpy.typing import NDArray
 
 
-Dims = TypeVarTuple("Dims")
-
-
-def grid_tiles(
+def grid_tiles[*Dims](
     slide_extent: tuple[int, *Dims],
     tile_extent: tuple[int, *Dims],
     stride: tuple[int, *Dims],
diff --git a/tests/test_mask_builders.py b/tests/test_mask_builders.py
new file mode 100644
index 0000000..0958db4
--- /dev/null
+++ b/tests/test_mask_builders.py
@@ -0,0 +1,368 @@
+import tempfile
+from pathlib import Path
+
+import numpy as np
+import pytest
+
+from ratiopath.masks.mask_builders import MaskBuilder, MaxAggregator, MeanAggregator
+
+
+@pytest.mark.parametrize("mask_extents", [(16, 16), (32, 64)])
+@pytest.mark.parametrize("channels", [1, 3, 8])
+@pytest.mark.parametrize("mask_tile_extents", [(4, 4), (4, 8), (8, 4), (3, 6), (9, 6)])
+@pytest.mark.parametrize("mask_tile_strides", [(2, 2), (3, 3), (3, 2), (2, 3)])
+def test_scalar_uniform_averaging_2d(
+    mask_extents, channels, mask_tile_extents, mask_tile_strides
+):
+    mask_extents = np.asarray(mask_extents)
+    mask_tile_extents = np.asarray(mask_tile_extents)
+    mask_tile_strides = np.asarray(mask_tile_strides)
+
+    batch_size = 4
+    num_batches = 8
+
+    gcds = np.gcd(mask_tile_extents, mask_tile_strides)
+    adjusted_tile_extents = mask_tile_extents // gcds
+
+    test_mask_builder = MaskBuilder(
+        source_extents=tuple(mask_extents),
+        source_tile_extent=tuple(mask_tile_extents),
+        output_tile_extent=(1, 1),
+        stride=tuple(mask_tile_strides),
+        n_channels=channels,
+        storage="inmemory",
+        aggregation=MeanAggregator,
+        dtype=np.float32,
+    )
+
+    # create scalar batch (B, C)
+    example = np.ones((batch_size, channels), dtype=np.float32)
+
+    for i in range(num_batches):
+        coords_batch = np.random.rand(batch_size, len(mask_extents))
+        coords_batch *= (np.array(mask_extents) - np.array(mask_tile_extents))[
+            np.newaxis, :
+        ]
+        # force alignment to gcds
+        coords_batch = (coords_batch.astype(np.int64) // gcds[np.newaxis, :]) * gcds[
+            np.newaxis, :
+        ]
+
+        test_mask_builder.update_batch(example, coords_batch)
+
+        assert np.isclose(
+            test_mask_builder.storage.sum(),
+            (i + 1) * batch_size * channels * np.prod(adjusted_tile_extents),
+            atol=1e-6,
+        ), (
+            f"Accumulator sum after batch {i + 1} should be {(i + 1) * batch_size * channels * np.prod(adjusted_tile_extents)}, is {test_mask_builder.storage.sum()}"
+        )
+
+    result = test_mask_builder.finalize()
+    # assembled has shape (channels, H_adj, W_adj)
+    assert result["mask"].shape[0] == channels, (
+        "Assembled mask should have correct number of channels"
+    )
+    assert result["overlap_counter"].shape[0] == 1, (
+        "Overlap accumulator should have single channel"
+    )
+    assert np.isclose(
+        result["overlap_counter"].sum(),
+        num_batches * batch_size * np.prod(adjusted_tile_extents),
+        atol=1e-6,
+    ), (
+        f"Overlap sum should be {num_batches * batch_size * np.prod(adjusted_tile_extents)}, is {result['overlap_counter'].sum()}"
+    )
+    assert np.isclose(result["mask"].max(), 1.0, atol=1e-6), (
+        f"Assembled max should be close to 1.0, is {result['mask'].max()}"
+    )
+    test_mask_builder.cleanup()
+
+
+@pytest.mark.parametrize("shape", [(1, 16, 16), (3, 32, 64), (8, 400, 35)])
+@pytest.mark.parametrize("mask_tile_extents", [(4, 4), (4, 8), (8, 4), (3, 6), (9, 6)])
+@pytest.mark.parametrize("mask_tile_strides", [(2, 2), (3, 3), (3, 2), (2, 3)])
+def test_scalar_uniform_max_2d(shape, mask_tile_extents, mask_tile_strides):
+    channels = shape[0]
+    source_extents = np.asarray(shape[1:])
+    source_tile_extent = np.asarray(mask_tile_extents)
+    stride = np.asarray(mask_tile_strides)
+    output_tile_extent = 1
+
+    batch_size = 4
+    num_batches = 10
+
+    test_mask_builder = MaskBuilder(
+        source_extents=tuple(source_extents),
+        source_tile_extent=tuple(source_tile_extent),
+        output_tile_extent=output_tile_extent,
+        stride=tuple(stride),
+        n_channels=channels,
+        storage="inmemory",
+        aggregation=MaxAggregator,
+        dtype=np.float32,
+    )
+
+    for i in range(num_batches):
+        coordinates_batch = np.random.rand(batch_size, len(source_extents))
+        coordinates_batch *= (source_extents - source_tile_extent)[np.newaxis, :]
+        # align to strides
+        coordinates_batch = (
+            coordinates_batch.astype(np.int64) // stride[np.newaxis, :]
+        ) * stride[np.newaxis, :]
+
+        value = float(i + 1)
+        data = np.full((batch_size, channels), value, dtype=np.float32)
+        test_mask_builder.update_batch(data, coordinates_batch)
+
+        accumulator = test_mask_builder.storage
+        assert accumulator.max() == value, (
+            f"Max value in accumulator after batch {i + 1} should be {value}, is {accumulator.max()}"
+        )
+
+    accumulator = test_mask_builder.storage
+    nonfinal_acc = accumulator.copy()
+    final_acc = test_mask_builder.finalize()
+    # final_acc should contain the maximum value (num_batches)
+    assert (final_acc == nonfinal_acc).all(), (
+        "Finalized accumulator should be equal to non-finalized accumulator for max aggregation"
+    )
+    test_mask_builder.cleanup()
+
+
+@pytest.mark.parametrize("clip", [0, 1, 3])
+@pytest.mark.parametrize("tile_extents", [(8, 8), (12, 9)])
+@pytest.mark.parametrize("channels", [1, 5])
+@pytest.mark.parametrize("mask_extents", [(32, 64), (64, 32), (100, 101)])
+@pytest.mark.parametrize("filename", [None, "test_heatmap.npy"])
+@pytest.mark.parametrize("overlap_counter_filename", [None, "test_overlap.counter.npy"])
+def test_edge_clipping_heatmap_assembler(
+    clip,
+    tile_extents,
+    channels,
+    mask_extents,
+    filename,
+    overlap_counter_filename,
+    tmp_path,
+):
+    source_extents = np.asarray(mask_extents)
+    source_tile_extent = np.asarray(tile_extents)
+    stride = np.asarray((4, 4), dtype=np.int64)
+    # Dense output case
+    output_tile_extent = source_tile_extent
+
+    num_batches = 4
+    batch_size = 8
+
+    if filename is not None:
+        tmp_path.mkdir(parents=True, exist_ok=True)
+        filename = Path((tmp_path / filename).as_posix())
+    if overlap_counter_filename is not None:
+        tmp_path.mkdir(parents=True, exist_ok=True)
+        overlap_counter_filename = Path(
+            (tmp_path / overlap_counter_filename).as_posix()
+        )
+
+    assembler = MaskBuilder(
+        source_extents=tuple(source_extents),
+        source_tile_extent=tuple(source_tile_extent),
+        output_tile_extent=tuple(output_tile_extent),
+        stride=tuple(stride),
+        n_channels=channels,
+        storage="memmap",
+        aggregation=MeanAggregator,
+        dtype=np.float32,
+        storage_kwargs={"filename": filename},
+        aggregation_kwargs={"overlap_counter_filename": overlap_counter_filename},
+    )
+
+    # create dummy data
+    example_tile_batch = np.ones(
+        (batch_size, channels, *output_tile_extent), dtype=np.float32
+    )
+
+    # clipped sum per tile in mask resolution
+    # Note: clip is in output (mask) resolution
+    clipped_tile_extent = output_tile_extent - 2 * clip
+    increment = batch_size * channels * np.prod(clipped_tile_extent)
+
+    # add the tiles randomly to cover the heatmap
+    for i in range(num_batches):
+        coords = np.random.rand(batch_size, len(source_extents))
+        coords *= (source_extents - source_tile_extent)[np.newaxis, :]
+        coords = coords.astype(np.int64)
+        assembler.update_batch(example_tile_batch, coords, edge_clipping=clip)
+
+        accumulator = assembler.storage
+        assert np.isclose(accumulator.sum(), (i + 1) * increment, atol=1e-5), (
+            f"Checksum mismatch in heatmap accumulator after update {i + 1}. Is {accumulator.sum()}, but expected {(i + 1) * increment}"
+        )
+
+    results = assembler.finalize()
+    assembled_heatmap = results["mask"]
+    overlap_counter = results["overlap_counter"]
+
+    assert assembled_heatmap.shape == (
+        channels,
+        *assembler.mask_extents,
+    ), (
+        f"Assembled heatmap has incorrect shape: {assembled_heatmap.shape}, shall be {(channels, *assembler.mask_extents)}"
+    )
+    assert overlap_counter.shape == (1, *assembler.mask_extents), (
+        f"Overlap counter has incorrect shape: {overlap_counter.shape}, shall be {(1, *assembler.mask_extents)}"
+    )
+    assert np.isclose(
+        overlap_counter.sum(),
+        num_batches * batch_size * np.prod(clipped_tile_extent),
+        atol=1e-5,
+    ), (
+        f"Checksum mismatch in overlap counter after finalization. Is {overlap_counter.sum()}, but expected {num_batches * batch_size * np.prod(clipped_tile_extent)}"
+    )
+    assert assembled_heatmap.max() == 1.0, (
+        f"Assembled heatmap values should be normalized to 1.0 after finalization: {assembled_heatmap.max()}"
+    )
+    assembler.cleanup()
+
+
+def test_edge_clipping_clips_edges():
+    """Simple test that ensures, that a tile put at [0,0], if clipped, does not write to the [0,0] corner of the heatmap."""
+    clip = 1
+    channels = 1
+    source_extents = (16, 16)
+    source_tile_extent = (8, 8)
+    output_tile_extent = (8, 8)
+    stride = (4, 4)
+
+    assembler = MaskBuilder(
+        source_extents=source_extents,
+        source_tile_extent=source_tile_extent,
+        output_tile_extent=output_tile_extent,
+        stride=stride,
+        n_channels=channels,
+        storage="memmap",
+        aggregation=MeanAggregator,
+        dtype=np.float32,
+    )
+
+    tile = np.ones((1, channels, *output_tile_extent), dtype=np.float32)
+    assembler.update_batch(tile, coords=np.asarray([[0, 0]]), edge_clipping=clip)
+    results = assembler.finalize()
+    assembled_heatmap = results["mask"]
+    overlap_counter = results["overlap_counter"]
+
+    assert (assembled_heatmap[0, 0] == 0.0).all(), (
+        "Top-left corner of assembled heatmap should be zero due to clipping"
+    )
+    assert (overlap_counter[0, 0] == 0.0).all(), (
+        "Top-left corner of overlap counter should be zero due to clipping"
+    )
+    assembler.cleanup()
+
+
+@pytest.mark.parametrize(
+    "source_extents, source_tile_extent, output_tile_extent, stride, expected_mask_extents",
+    [
+        # Divisible extents: (source_extents - source_tile_extent) is divisible by stride.
+        ((33, 53), (8, 8), (4, 4), (4, 4), (18, 28)),
+        ((33, 53), (8, 8), (1, 1), (4, 4), (9, 14)),
+    ],
+)
+def test_storage_shape_is_minimal_for_extent_and_stride(
+    source_extents,
+    source_tile_extent,
+    output_tile_extent,
+    stride,
+    expected_mask_extents,
+):
+    """Storage shape must be the smallest shape that can fit all tiled outputs."""
+    builder = MaskBuilder(
+        source_extents=source_extents,
+        source_tile_extent=source_tile_extent,
+        output_tile_extent=output_tile_extent,
+        stride=stride,
+        n_channels=2,
+        storage="inmemory",
+        aggregation=MeanAggregator,
+        dtype=np.float32,
+    )
+
+    expected_mask_extents_arr = np.asarray(expected_mask_extents, dtype=np.int64)
+
+    assert np.array_equal(builder.mask_extents, expected_mask_extents_arr), (
+        f"Mask extents {builder.mask_extents} should be minimal {expected_mask_extents_arr}"
+    )
+    assert builder.storage.shape == (2, *expected_mask_extents), (
+        f"Storage shape {builder.storage.shape} should be {(2, *expected_mask_extents)}"
+    )
+
+    builder.cleanup()
+
+
+def test_numpy_memmap_tempfile_management(monkeypatch):
+    """Test that temporary files created by NamedTemporaryFile are properly deleted."""
+    captured_files = []
+    original_namedtempfile = tempfile.NamedTemporaryFile
+
+    def intercepting_namedtempfile(*args, **kwargs):
+        temp_file = original_namedtempfile(*args, **kwargs)
+        captured_files.append(Path(temp_file.name))
+        return temp_file
+
+    monkeypatch.setattr(tempfile, "NamedTemporaryFile", intercepting_namedtempfile)
+
+    assembler = MaskBuilder(
+        source_extents=(16, 16),
+        source_tile_extent=(8, 8),
+        output_tile_extent=(8, 8),
+        stride=(4, 4),
+        n_channels=1,
+        storage="memmap",
+        aggregation=MeanAggregator,
+        dtype=np.float32,
+    )
+
+    assert len(captured_files) >= 2, (
+        "Expected at least two temporary files for main and overlap accumulators"
+    )
+    temp_filepaths = captured_files.copy()
+    tile = np.ones((1, 1, 8, 8), dtype=np.float32)
+    assembler.update_batch(tile, coords=np.asarray([[0, 0]]), edge_clipping=1)
+    assembler.cleanup()
+    for temp_filepath in temp_filepaths:
+        assert not temp_filepath.exists(), (
+            f"Temporary file {temp_filepath} should be deleted"
+        )
+
+
+def test_numpy_memmap_persistent_file(tmp_path):
+    """Test that a persistent file created by a composed memmap builder is not deleted upon finalization."""
+    filename = tmp_path / "persistent_heatmap.npy"
+
+    assembler = MaskBuilder(
+        source_extents=(16, 16),
+        source_tile_extent=(8, 8),
+        output_tile_extent=(8, 8),
+        stride=(4, 4),
+        n_channels=1,
+        storage="memmap",
+        aggregation=MeanAggregator,
+        dtype=np.float32,
+        filename=filename,
+        overlap_counter_filename=filename.with_suffix(".overlaps" + filename.suffix),
+    )
+
+    tile_batch = np.ones((1, 1, 8, 8), dtype=np.float32)
+    assembler.update_batch(tile_batch, coords=np.asarray([[0, 0]]), edge_clipping=1)
+
+    assembler.cleanup()
+
+    assert filename.exists(), (
+        f"Persistent file {filename} should exist after finalization"
+    )
+    assert filename.with_suffix(".overlaps" + filename.suffix).exists(), (
+        f"Persistent overlap file {filename.with_suffix('.overlaps' + filename.suffix)} should exist after finalization"
+    )
+
+    # Clean up
+    filename.unlink()
+    filename.with_suffix(".overlaps" + filename.suffix).unlink()
diff --git a/uv.lock b/uv.lock
index 6e32abc..53fd50a 100644
--- a/uv.lock
+++ b/uv.lock
@@ -2,9 +2,9 @@ version = 1
 revision = 3
 requires-python = ">=3.12, <3.14"
 resolution-markers = [
-    "sys_platform == 'darwin'",
-    "platform_machine == 'aarch64' and sys_platform == 'linux'",
-    "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')",
+    "sys_platform == 'win32'",
+    "sys_platform == 'emscripten'",
+    "sys_platform != 'emscripten' and sys_platform != 'win32'",
 ]
 
 [[package]]
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