EmbedTrack—Simultaneous Cell Segmentation and Tracking Through Learning Offsets and Clustering Bandwidths

A systematic analysis of the cell behavior requires automated approaches for cell segmentation and tracking. While deep learning has been successfully applied for the task of cell segmentation, there are few approaches for simultaneous cell segmentation and tracking using deep learning. Here, we present EmbedTrack, a single convolutional neural network for simultaneous cell segmentation and tracking which predicts easy to interpret embeddings. As embeddings, offsets of cell pixels to their cell center and bandwidths are learned. We benchmark our approach on nine 2D data sets from the Cell Tracking Challenge, where our approach performs on seven out of nine data sets within the top 3 contestants including three top 1 performances. The source code is publicly available at https://git.scc.kit.edu/kit-loe-ge/embedtrack.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Computer Vision Approaches for Mapping Gene Expression onto Lineage Trees

This project concerns studying the early development of living organisms. This period is accompanied by dynamic morphogenetic events. There is an increase in the number of cells, changes in the shape of cells and specification of cell fate during this time. Typically, in order to capture the dynamic morphological changes, one can employ a form of microscopy imaging such as Selective Plane Illumination Microscopy (SPIM) which offers a single-cell resolution across time, and hence allows observing the positions, velocities and trajectories of most cells in a developing embryo. Unfortunately, the dynamic genetic activity which underlies these morphological changes and influences cellular fate decision, is captured only as static snapshots and often requires processing (sequencing or imaging) multiple distinct individuals. In order to set the stage for characterizing the factors which influence cellular fate, one must bring the data arising from the above-mentioned static snapshots of multiple individuals and the data arising from SPIM imaging of other distinct individual(s) which characterizes the changes in morphology, into the same frame of reference. In this project, a computational pipeline is established, which achieves the aforementioned goal of mapping data from these various imaging modalities and specimens to a canonical frame of reference. This pipeline relies on the three core building blocks of Instance Segmentation, Tracking and Registration. In this dissertation work, I introduce EmbedSeg which is my solution to performing instance segmentation of 2D and 3D (volume) image data. Next, I introduce LineageTracer which is my solution to performing tracking of a time-lapse (2d+t, 3d+t) recording. Finally, I introduce PlatyMatch which is my solution to performing registration of volumes. Errors from the application of these building blocks accumulate which produces a noisy observation estimate of gene expression for the digitized cells in the canonical frame of reference. These noisy estimates are processed to infer the underlying hidden state by using a Hidden Markov Model (HMM) formulation. Lastly, for wider dissemination of these methods, one requires an effective visualization strategy. A few details about the employed approach are also discussed in the dissertation work. The pipeline was designed keeping imaging volume data in mind, but can easily be extended to incorporate other data modalities, if available, such as single cell RNA Sequencing (scRNA-Seq) (more details are provided in the Discussion chapter). The methods elucidated in this dissertation would provide a fertile playground for several experiments and analyses in the future. Some of such potential experiments and current weaknesses of the computational pipeline are also discussed additionally in the Discussion Chapter

STEm-Seg: Spatio-temporal Embeddings for Instance Segmentation in Videos

Existing methods for instance segmentation in videos typi-cally involve multi-stage pipelines that follow the tracking-by-detectionparadigm and model a video clip as a sequence of images. Multiple net-works are used to detect objects in individual frames, and then associatethese detections over time. Hence, these methods are often non-end-to-end trainable and highly tailored to specific tasks. In this paper, we pro-pose a different approach that is well-suited to a variety of tasks involvinginstance segmentation in videos. In particular, we model a video clip asa single 3D spatio-temporal volume, and propose a novel approach thatsegments and tracks instances across space and time in a single stage. Ourproblem formulation is centered around the idea of spatio-temporal em-beddings which are trained to cluster pixels belonging to a specific objectinstance over an entire video clip. To this end, we introduce (i) novel mix-ing functions that enhance the feature representation of spatio-temporalembeddings, and (ii) a single-stage, proposal-free network that can rea-son about temporal context. Our network is trained end-to-end to learnspatio-temporal embeddings as well as parameters required to clusterthese embeddings, thus simplifying inference. Our method achieves state-of-the-art results across multiple datasets and tasks. Code and modelsare available at https://github.com/sabarim/STEm-Seg.Comment: 28 pages, 6 figure