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Abstract
Generalized nucleus segmentation techniques can contribute greatly to reducing the time to develop and validate visual biomarkers for new digital pathology datasets. We summarize the results of MoNuSeg 2018 Challenge whose objective was to develop generalizable nuclei segmentation techniques in digital pathology. The challenge was an official satellite event of the MICCAI 2018 conference in which 32 teams with more than 80 participants from geographically diverse institutes participated. Contestants were given a training set with 30 images from seven organs with annotations of 21,623 individual nuclei. A test dataset with 14 images taken from seven organs, including two organs that did not appear in the training set was released without annotations. Entries were evaluated based on average aggregated Jaccard index (AJI) on the test set to prioritize accurate instance segmentation as opposed to mere semantic segmentation. More than half the teams that completed the challenge outperformed a previous baseline. Among the trends observed that contributed to increased accuracy were the use of color normalization as well as heavy data augmentation. Additionally, fully convolutional networks inspired by variants of U-Net, FCN, and Mask-RCNN were popularly used, typically based on ResNet or VGG base architectures. Watershed segmentation on predicted semantic segmentation maps was a popular post-processing strategy. Several of the top techniques compared favorably to an individual human annotator and can be used with confidence for nuclear morphometrics.
Highlights
Examination of Hematoxylin and eosin (H&E) stained tissue under a microscope remains the mainstay of pathology
We present a summary of the techniques used by 32 teams who successfully completed the challenge
We describe the trends observed in pre-processing, data augmentation, modeling, task specification, optimization, and postprocessing techniques used by the teams
Summary
Examination of H&E stained tissue under a microscope remains the mainstay of pathology. There is far too much variation in the appearance of nuclei and their surroundings by organs, disease conditions, and even digital scanner brands or histology technicians Examples of such variations are shown, along with the problems of some common segmentation algorithms such as Otsu thresholding [11], marker controlled watershed segmentation [12]–[14] or open-source packages like Fiji [15] and Cell Profiler [16]. Kumar et al introduced a metric called Aggregated Jaccard Index (AJI) that is more appropriate to evaluate algorithms for this instance segmentation problem as opposed to other popular metrics such as Dice coefficient, which are more suited for semantic segmentation problems This is because nucleus segmentation algorithms should tell the difference between nuclear and non-nuclear pixels, but they should be able to tell pixels belonging to two nuclei apart that touch or overlap with each other. They had released a trained model that performed reasonably well on unseen organs from the test subset of images.
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