EmbryosFormer: Deformable Transformer and Collaborative Encoding-Decoding for Embryos Stage Development Classification

The timing of cell divisions in early embryos during the In-Vitro Fertilization (IVF) process is a key predictor of embryo viability. However, observing cell divisions in Time-Lapse Monitoring (TLM) is a time-consuming process and highly depends on experts. In this paper, we propose EmbryosFormer, a computational model to automatically detect and classify cell divisions from original time-lapse images. Our proposed network is designed as an encoder-decoder deformable transformer with collaborative heads. The transformer contracting path predicts per-image labels and is optimized by a classification head. The transformer expanding path models the temporal coherency between embryo images to ensure monotonic non-decreasing constraint and is optimized by a segmentation head. Both contracting and expanding paths are synergetically learned by a collaboration head. We have benchmarked our proposed EmbryosFormer on two datasets: a public dataset with mouse embryos with 8-cell stage and an in-house dataset with human embryos with 4-cell stage. Source code: https://github.com/UARK-AICV/Embryos.Comment: Accepted at WACV 202

Automating assessment of human embryo images and time-lapse sequences for IVF treatment

As the number of couples using In Vitro Fertilization (IVF) treatment to give birth increases, so too does the need for robust tools to assist embryologists in selecting the highest quality embryos for implantation. Quality scores assigned to embryonic structures are critical markers for predicting implantation potential of human blastocyst-stage embryos. Timing at which embryos reach certain cell and development stages in vitro also provides valuable information about their development progress and potential to become a positive pregnancy. The current workflow of grading blastocysts by visual assessment is susceptible to subjectivity between embryologists. Visually verifying when embryo cell stage increases is tedious and confirming onset of later development stages is also prone to subjective assessment. This thesis proposes methods to automate embryo image and time-lapse sequence assessment to provide objective evaluation of blastocyst structure quality, cell counting, and timing of development stages

Deep learning-enabled technologies for bioimage analysis.

Deep learning (DL) is a subfield of machine learning (ML), which has recently demonstrated its potency to significantly improve the quantification and classification workflows in biomedical and clinical applications. Among the end applications profoundly benefitting from DL, cellular morphology quantification is one of the pioneers. Here, we first briefly explain fundamental concepts in DL and then we review some of the emerging DL-enabled applications in cell morphology quantification in the fields of embryology, point-of-care ovulation testing, as a predictive tool for fetal heart pregnancy, cancer diagnostics via classification of cancer histology images, autosomal polycystic kidney disease, and chronic kidney diseases

Scalable Inference for Multi-Target Tracking of Proliferating Cells

With the continuous advancements in microscopy techniques such as improved image quality, faster acquisition and reduced photo-toxicity, the amount of data recorded in the life sciences is rapidly growing. Clearly, the size of the data renders manual analysis intractable, calling for automated cell tracking methods. Cell tracking – in contrast to other tracking scenarios – exhibits several difficulties: low signal to noise ratio in the images, high cell density and sometimes cell clusters, radical morphology changes, but most importantly cells divide – which is often the focus of the experiment. These peculiarities have been targeted by tracking-byassignment methods that first extract a set of detection hypotheses and then track those over time. Improving the general quality of these cell tracking methods is difficult, because every cell type, surrounding medium, and microscopy setting leads to recordings with specific properties and problems. This unfortunately implies that automated approaches will not become perfect any time soon but manual proof reading by experts will remain necessary for the time being. In this thesis we focus on two different aspects, firstly on scaling previous and developing new solvers to deal with longer videos and more cells, and secondly on developing a specialized pipeline for detecting and tracking tuberculosis bacteria. The most powerful tracking-by-assignment methods are formulated as probabilistic graphical models and solved as integer linear programs. Because those integer linear programs are in general NP-hard, increasing the problem size will lead to an explosion of computational cost. We begin by reformulating one of these models in terms of a constrained network flow, and show that it can be solved more efficiently. Building on the successful application of network flow algorithms in the pedestrian tracking literature, we develop a heuristic to integrate constraints – here for divisions – into such a network flow method. This allows us to obtain high quality approximations to the tracking solution while providing a polynomial runtime guarantee. Our experiments confirm this much better scaling behavior to larger problems. However, this approach is single threaded and does not utilize available resources of multi-core machines yet. To parallelize the tracking problem we present a simple yet effective way of splitting long videos into intervals that can be tracked independently, followed by a sparse global stitching step that resolves disagreements at the cuts. Going one step further, we propose a microservices based software design for ilastik that allows to distribute all required computation for segmentation, object feature extraction, object classification and tracking across the nodes of a cluster or in the cloud. Finally, we discuss the use case of detecting and tracking tuberculosis bacteria in more detail, because no satisfying automated method to this important problem existed before. One peculiarity of these elongated cells is that they build dense clusters in which it is hard to outline individuals. To cope with that we employ a tracking-by-assignment model that allows competing detection hypotheses and selects the best set of detections while considering the temporal context during tracking. To obtain these hypotheses, we develop a novel algorithm that finds diverseM- best solutions of tree-shaped graphical models by dynamic programming. First experiments with the pipeline indicate that it can greatly reduce the required amount of human intervention for analyzing tuberculosis treatment