Handling incorrect non binary labellling when labelling objects in a greyscale image by tile

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I am working on a project where I have a model which does instance segmentation to segment nuclei in a image. Next step would be to label these segmented nuclei. I am scaling the labeling by processing images as tiles.

The issue I am facing now is to come up with a way to handle incorrect labeling. Basically , when there is a object which gets split due to tiling they are labelled differently .

tile_size = 2048
  for x in range(0, vec_arr.shape[2], tile_size):
                        x_max = min([vec_arr.shape[2], x + tile_size])
                        for y in range(0, vec_arr.shape[1], tile_size):
                            y_max = min([vec_arr.shape[1], y + tile_size])

The above code explains how I am tiling a image. I am using this repo(https://github.com/MouseLand/cellpose/blob/master/cellpose/dynamics.py#L574) as the basis for labeling images since I am using their network. I am looking for ideas on how I can identify objects which are connected across tiles and fill them with same values.

Currently I maintain a counter of number of objects labelled in a tile and start labeling from that value.

I am interested in knowing on how I can identify same objects across tiles.

imagej computer-vision opencv python-3.x
1 Answers

This is not easy.

First of all you need an overlap in your tiling. Each tile should overlap the surrounding ones by some amount, which you then cut off when recomposing the larger image. The overlap amount should be at least the size of a nucleus, but preferably larger. The extra space is meant to guarantee that a nucleus that straddles the tile edge is detected identically in the two tiles where you can see it.

Next, when cutting off the overlap region and decomposing the larger image, a nucleus that straddles the tile edge (is partially in the overlap region) must be either preserved entirely or removed completely depending on which tile it “belongs to”. There are different ways to define this. For example, you can compute the centroid of the nucleus, and determine in which tile that is, and remove the nucleus from the other tile.

Thus, each nucleus is detected in exactly one tile. However, if the overlap region is not large enough, then a detection for a nucleus might not have the same shape in the two overlapping tiles, leading to two different centroids for the same nucleus. In this case, the nucleus could be perceived as not part of either tile, or part of both tiles. It is important to understand the detection algorithm, so that you can find the right overlap size that will guarantee identical detection for the two tiles.

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