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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.

Understanding and controlling the self-assembly of vertically oriented carbon nanotube (CNT) forests is essential for realizing their potential in myriad applications. The governing process–structure–property mechanisms are poorly understood, and the processing parameter space is far too vast to exhaustively explore experimentally. We overcome these limitations by using a physics-based simulation as a high-throughput virtual laboratory and image-based machine learning to relate CNT forest synthesis attributes to their mechanical performance. Using CNTNet, our image-based deep learning classifier module trained with synthetic imagery, combinations of CNT diameter, density, and population growth rate classes were labeled with an accuracy of >91%. The CNTNet regression module predicted CNT forest stiffness and buckling load properties with a lower root-mean-square error than that of a regression predictor based on CNT physical parameters. These results demonstrate that image-based machine learning trained using only simulated imagery can distinguish subtle CNT forest morphological features to predict physical material properties with high accuracy. CNTNet paves the way to incorporate scanning electron microscope imagery for high-throughput material discovery.

Accurate segmentation and tracking of cells in microscopy image sequences is extremely beneficial in clinical diagnostic applications and biomedical research. A continuing challenge is the segmentation of dense touching cells and deforming cells with indistinct boundaries, in low signal-to-noise-ratio images. In this paper, we present a dual-stream marker-guided network (DMNet) for segmentation of touching cells in microscopy videos of many cell types. DMNet uses an explicit cell marker-detection stream, with a separate mask-prediction stream using a distance map penalty function, which enables supervised training to focus attention on touching and nearby cells. For multi-object cell tracking we use M2Track tracking-by-detection approach with multi-step data association. Our M2Track with mask overlap includes short term track-to-cell association followed by track-to-track association to re-link tracklets with missing segmentation masks over a short sequence of frames. Our combined detection, segmentation and tracking algorithm has proven its potential on the IEEE ISBI 2021 6th Cell Tracking Challenge (CTC-6) where we achieved multiple top three rankings for diverse cell types. Our team name is MU-Ba-US, and the implementation of DMNet is available at, http://celltrackingchallenge.net/participants/MU-Ba-US/.

We propose a novel deep architecture for semantic labeling of 3D point clouds referred to as Global and Local Streams Network (GLSNet) which is designed to capture both global and local structures and contextual information for large scale 3D point cloud classification. Our GLSNet tackles a hard problem – large differences of object sizes in large-scale point cloud segmentation including extremely large objects like water, and small objects like buildings and trees, and we design a two-branch deep network architecture to decompose the complex problem to separate processing problems at global and local scales and then fuse their predictions. GLSNet combines the strength of Submanifold Sparse Convolutional Network [1] for learning global structure with the strength of PointNet++ [2] for incorporating local information.The first branch of GLSNet processes a full point cloud in the global stream, and it captures long range information about the geometric structure by using a U-Net structured Submanifold Sparse Convolutional Network (SSCN-U) architecture. The second branch of GLSNet processes a point cloud in the local stream, and it partitions 3D points into slices and processes one slice of the cloud at a time by using the PointNet ++ architecture. The two streams of information are fused by max pooling over their classification prediction vectors. Our results on the IEEE GRSS Data Fusion Contest Urban Semantic 3D, Track 4 (DFT4) [3] [4] [5] point cloud classification dataset have shown that GLSNet achieved performance gains of almost 4% in mIOU and 1% in overall accuracy over the individual streams on the held-back testing dataset.