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Add label merging function (and improve ndmeasure.label)#409

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m-albert:improve_labels_and_add_merge_functionality
Open

Add label merging function (and improve ndmeasure.label)#409
m-albert wants to merge 6 commits into
dask:mainfrom
m-albert:improve_labels_and_add_merge_functionality

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@m-albert

@m-albert m-albert commented Sep 9, 2025 •
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PR

This PR contains work I've been wanting to discuss and push here for a while.

Highlights:

  1. Improvements to ndmeasure.label:
    1. Implementation of the idea by @jakirkham and @jni to make labels unique by using the lower and upper bits of the label array (see here). This improves parallelism at the expensive of returning non-contiguous labels.
    2. The current implementation of ndmeasure.label doesn't output contiguous (sequential) labels (also before implementing the point above). This PR adds a parameter produce_sequential_labels to dask_image.ndmeasure.label to output contiguous labels.
  2. Implementation of a chunked version of skimage.segmentation.relabel_sequential: ndmeasure.relabel_sequential (this provides the functionality e.g. of the point above, happy to discuss whether the function should be public)
  3. Implementation of ndmeasure.merge_labels_across_chunk_boundaries (details below)
    1. Use case 1: merge touching labels
    2. Use case 2: merge labels above a given IoU within the overlap.

merge_labels_across_chunk_boundaries

What's the idea behind ndmeasure.merge_labels_across_chunk_boundaries?

Visual abstract

Cases of overlap = 0 (top) and overlap > 0 (bottom)

image

Description

From the docstring:

def merge_labels_across_chunk_boundaries(
        labels,
        overlap_depth=0,
        iou_threshold=0.8,
        structure=None,
        wrap_axes=None,
        trim_overlap=True,
        produce_sequential_labels=True,
        ):
    """
    Merge labels across chunk boundaries.

    Each chunk in ``labels`` has been labeled independently, and the labels
    in different chunks may overlap. This function tries to merge labels across
    chunk boundaries using a strategy dependent on ``overlap``:
    - If ``overlap > 0``, the overlap region between chunks is used to
      determine which between each pair of chunks should be merged.
    - If ``overlap == 0``, labels that touch across chunk boundaries are merged.

    Parameters
    ----------
    labels : dask array of int
        The input labeled array, where each chunk is independently labeled.
    overlap_depth : int, optional
        The size of the overlap region between chunks, e.g. as produced by
        `dask.array.overlap` or `map_overlap`. Default is 0.
    iou_threshold : float, optional
        If ``overlap > 0``, the intersection-over-union (IoU) between labels
        in the overlap region is used to determine which labels should be
        merged. If the IoU between two labels is greater than ``iou_threshold``,
        they are merged. Default is 0.8.
    structure : array of bool, optional
        Structuring element for determining connectivity. If None, a
        cross-shaped structuring element is used.
    wrap_axes : tuple of int, optional
        Should labels be wrapped across array boundaries, and if so which axes.
        - (0,) only wrap over the 0th axis.
        - (0, 1) wrap over the 0th and 1st axis.
        - (0, 1, 3)  wrap over 0th, 1st and 3rd axis.
    trim_overlap : bool, optional
        If True, the overlap regions are trimmed from the output labels.
        Default is True.
    """

Example

Consider the following problem.

The user wants to segment a large input image, i.e. a dask array.

import numpy as np
import dask.array as da
import skimage
import matplotlib.pyplot as plt

dim = da.from_array(
    skimage.data.cells3d()[:, 1, :, :].max(axis=0), chunks=(110, 110))
image

and has a segmentation method that cannot be applied on the entire image (e.g. memory or method constraints) but works well on regions of the input image. The user can use map_overlap to apply the segmentation on the chunks of the input image and include overlap so that the segmentation method performs well on the chunk boundaries:

model = models.Cellpose(gpu=False, model_type='cyto')

dseg = dim.map_overlap(
        lambda x: model.eval(x, diameter=None, channels=[0, 0])[0],
        dtype=np.uint16,
        depth=10,
    ).persist()

plt.figure()
plt.imshow(dseg)
image

At this point, the user faces two challenges:

  1. the obtained segmentation labels show boundary artefacts and
  2. they are not unique.

This is where ndmeasure.merge_labels_across_chunk_boundaries comes in to merge labels across chunk boundaries.

First use case / usage pattern

Similarly to the implementation of ndmeasure.label and e.g. the approach provided in distributed cellpose (ping @GFleishman), one approach for merging labels consists in merging labels that touch across chunk boundaries. ndmeasure.merge_labels_across_chunk_boundaries allows to do this in a way that's agnostic of the segmentation method:

import dask_image.ndmeasure

dseg_merged = dask_image.ndmeasure.merge_labels_across_chunk_boundaries(
    dseg
)['labels']

plt.figure()
plt.imshow(dseg_merged)
image

Second use case

The approach above joins all labels that form part of the same connected component in a boundary slice of thickness one. This can lead to artefacts. In fact, the example image above contains two merged cells which are clearly separated in the chunkwise segmentation. To separate these, dask_image.ndmeasure.merge_labels_across_chunk_boundaries has the option to merge labels based on the IoU within an overlap region.

dseg = da.overlap.overlap(
        dim, depth={0: 10, 1: 10},
        boundary='none'
    ).map_blocks(
        lambda x: model.eval(x, diameter=None, channels=[0, 0])[0],
        dtype=np.uint16
    ).persist()

dseg_merged = dask_image.ndmeasure.merge_labels_across_chunk_boundaries(
    dseg, overlap_depth=10, iou_threshold=0.8,
)['labels'].compute(scheduler='single-threaded')

plt.figure()
plt.imshow(dseg_merged)
image

The labels of the two cells below which previously had been joined now remain separated.

Finally

In my personal projects it's been useful to have a function that merges labels across chunks. I think dask-image could be a good place for providing such functionality to the community and I've had some offline feedback at meetings that this could be useful. Happy about any further feedback / discussion over here!

@jni

jni commented Sep 19, 2025

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Hey @m-albert I am currently very overwhelmed with stuff but I wanted to say this is awesome 🤩 and something I have wanted for about 15 years!

@m-albert

m-albert commented Sep 20, 2025

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Thanks @jni 🤗 Glad you like it and that you had the same idea!

Regarding this PR I still need to get an (obscurely) failing test under control...

@m-albert

m-albert commented Oct 27, 2025 •
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Just pasting these svgs here that ChatGPT created 🤯

image
SVG code 
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m-albert added 6 commits June 3, 2026 12:17
@m-albert
Improve parallelism of label() by making block labels unique using lo…
f5522ac 
…w/high bit encoding rather than additive scheme
@m-albert
Refactor counting of remapped labels
9271db9
@m-albert
Introduce new public functions merge_labels_across_chunk_boundaries (…
ac3aee4 
…includes new functionality) and relabel_sequential. merge_labels_across_chunk_boundaries. Refactor ndmeasure.label to use the new functions. Rewrite relabel_blocks to handle sparse and large values relabeling efficiently. Add tests for new functionality.
@m-albert
Fix wrong overlap_depth handling
a65e6e0
@m-albert
Fix flake8
6361a9a
@m-albert
Fix compatibility with numpy<2
c5f57de
Copilot AI review requested due to automatic review settings June 3, 2026 10:18
@m-albert
m-albert force-pushed the improve_labels_and_add_merge_functionality branch from 66ede85 to c5f57de Compare June 3, 2026 10:18
Copilot AI reviewed Jun 3, 2026
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Pull request overview

This PR expands dask_image.ndmeasure’s labeling capabilities to better support chunkwise/overlapped segmentations by (1) improving ndmeasure.label parallelism/label-id behavior and (2) adding APIs to relabel and merge labels across chunk boundaries.

Changes:

  • Add produce_sequential_labels option to ndmeasure.label and introduce relabel_sequential.
  • Add merge_labels_across_chunk_boundaries to merge independently-labeled chunks (touching merge for overlap_depth=0, IoU-based merge for overlap_depth>0).
  • Add/extend tests covering the new behaviors and helpers.

Reviewed changes

Copilot reviewed 3 out of 3 changed files in this pull request and generated 11 comments.

File Description
dask_image/ndmeasure/__init__.py Adds/rewires labeling pipeline, introduces chunk-boundary merge + sequential relabeling utilities.
dask_image/ndmeasure/_utils/_label.py Implements core helpers for uniqueness encoding, adjacency discovery, connected-components mapping, and relabeling.
tests/test_dask_image/test_ndmeasure/test_core.py Updates label tests for sequential option; adds tests for uniqueness + chunk-boundary merging.

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Comment thread tests/test_dask_image/test_ndmeasure/test_core.py
Comment on lines 320 to +323
matching = np.stack((labels0.ravel(), labels1.ravel()), axis=1)
unique_matching = dask_image.ndmeasure._label._unique_axis(matching)
bincount0 = np.bincount(unique_matching[:, 0])
bincount1 = np.bincount(unique_matching[:, 1])
assert np.all(bincount0 == 1)
assert np.all(bincount1 == 1)
assert len(np.unique(unique_matching[:, 0])) == \
len(np.unique(unique_matching[:, 1]))
Comment thread tests/test_dask_image/test_ndmeasure/test_core.py
Comment on lines +875 to +877
np.random.seed(42)
labels = da.random.randint(0, 1, size=(6, 6), chunks=(3, 3))
unique_labels = dask_image.ndmeasure._label._make_labels_unique(labels)
Comment thread tests/test_dask_image/test_ndmeasure/test_core.py
Comment on lines +955 to +960
dim_segmentation_merged = dask_image.ndmeasure\
.merge_labels_across_chunk_boundaries(
dim_chunkwise_segmentation,
overlap_depth=overlap_depth,
iou_threshold=0,
)['labels']
Comment thread dask_image/ndmeasure/_utils/_label.py
Comment on lines 5 to 8
import dask.array as da
import numpy as np
import pandas as pd
import scipy.ndimage
Comment thread dask_image/ndmeasure/_utils/_label.py
Comment on lines +54 to +59
def relabel_block(block, mapping):
# Convert block to pandas Series for mapping
s = pd.Series(block.flatten())
# Vectorized lookup using reindex
relabeled_block = s.replace(mapping).to_numpy().reshape(block.shape)
return relabeled_block
Comment thread dask_image/ndmeasure/_utils/_label.py
Comment on lines +259 to 264
face_slice_infos = _chunk_faces(
labels.chunks, labels.shape, structure,
wrap_axes=wrap_axes, overlap_depth=overlap_depth
)
all_mappings = [da.empty((2, 0), dtype=LABEL_DTYPE, chunks=1)]

Comment thread dask_image/ndmeasure/_utils/_label.py
Comment on lines +482 to +486
"""
Make labels unique across blocks by using
- lower bits to encode the block ID
- higher bits to encode the label ID within the block

Comment thread dask_image/ndmeasure/_utils/_label.py
Comment on lines +508 to +514
block_id = np.ravel_multi_index(
block_id,
numblocks,
)
block = block.astype(encoding_dtype)
block = _encode_label(block, block_id, encoding_dtype=encoding_dtype)

Comment thread dask_image/ndmeasure/__init__.py
Comment on lines +526 to +528
mapping = delayed(
lambda x: {k: v for k, v in zip(x, np.arange(len(x)))})(u)

Comment thread dask_image/ndmeasure/__init__.py
Comment on lines +495 to +500
relabeled = _label.relabel(
block_labeled_unique, mapping
)

relabeled = relabeled.astype(_label.LABEL_DTYPE)

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3 participants

@m-albert @jni