The Cell Tracking Challenge: 10 years of objective benchmarking.

The Cell Tracking Challenge is an ongoing benchmarking initiative that has become a reference in cell segmentation and tracking algorithm development. Here, we present a significant number of improvements introduced in the challenge since our 2017 report. These include the creation of a new segmentation-only benchmark, the enrichment of the dataset repository with new datasets that increase its diversity and complexity, and the creation of a silver standard reference corpus based on the most competitive results, which will be of particular interest for data-hungry deep learning-based strategies. Furthermore, we present the up-to-date cell segmentation and tracking leaderboards, an in-depth analysis of the relationship between the performance of the state-of-the-art methods and the properties of the datasets and annotations, and two novel, insightful studies about the generalizability and the reusability of top-performing methods. These studies provide critical practical conclusions for both developers and users of traditional and machine learning-based cell segmentation and tracking algorithms.

Extending U-Net Network for Improved Nuclei Instance Segmentation Accuracy in Histopathology Images.

Analysis of morphometric features of nuclei plays an important role in understanding disease progression and predict efficacy of treatment. First step towards this goal requires segmentation of individual nuclei within the imaged tissue. Accurate nuclei instance segmentation is one of the most challenging tasks in computational pathology due to broad morphological variances of individual nuclei and dense clustering of nuclei with indistinct boundaries. It is extremely laborious and costly to annotate nuclei instances, requiring experienced pathologists to manually draw the contours, which often results in the lack of annotated data. Inevitably subjective annotation and mislabeling prevent supervised learning approaches to learn from accurate samples and consequently decrease the generalization capacity to robustly segment unseen organ nuclei, leading to over- or under-segmentations as a result. To address these issues, we use a variation of U-Net that uses squeeze and excitation blocks (USE-Net) for robust nuclei segmentation. The squeeze and excitation blocks allow the network to perform feature recalibration by emphasizing informative features and suppressing less useful ones. Furthermore, we extend the proposed network USE-Net not to generate only a segmentation mask, but also to output shape markers to allow better separation of nuclei from each other particularly within dense clusters. The proposed network was trained, tested, and evaluated on 2018 MICCAI Multi-Organ-Nuclei-Segmentation (MoNuSeg) challenge dataset. Promising results were obtained on unseen data despite that the data used for training USE-Net was significantly small. The source code of the USE-Net is available at https://github.com/CIVA-Lab/USE-Net.