Segmentation of Tissues and Proliferating Cells in Light-Sheet Microscopy Images of Mouse Embryos Using Convolutional Neural Networks

An interpretable framework for inter-observer agreement measurements in TILs scoring on histopathological breast images: A proof-of-principle study

Breast cancer, a widespread and life-threatening disease, necessitates precise diagnostic tools for improved patient outcomes. Tumor-Infiltrating Lymphocytes (TILs), reflective of the immune response against cancer cells, are pivotal in understanding breast cancer behavior. However, inter-observer variability in TILs scoring methods poses challenges to reliable assessments. This study introduces a novel and interpretable proof-of-principle framework comprising two innovative inter-observer agreement measures. The first method, Boundary-Weighted Fleiss' Kappa (BWFK), addresses tissue segmentation predictions, focusing on mitigating disagreements along tissue boundaries. BWFK enhances the accuracy of stromal segmentation, providing a nuanced assessment of inter-observer agreement. The second proposed method, the Distance Based Cell Agreement Algorithm (DBCAA), eliminates the need for ground truth annotations in cell detection predictions. This innovative approach offers versatility across histopathological analyses, overcoming data availability challenges. Both methods were applied to assess inter-observer agreement using a clinical image dataset consisting of 25 images of invasive ductal breast carcinoma tissue, each annotated by four pathologists, serving as a proof-of-principle. Experimental investigations demonstrated that the BWFK method yielded gains of up to 32% compared to the standard Fleiss' Kappa model. Furthermore, a procedure for conducting clinical validations of artificial intelligence (AI) based cell detection methods was elucidated. Thoroughly validated on a clinical dataset, the framework contributes to standardized, reliable, and interpretable inter-observer agreement assessments. This study is the first examination of inter-observer agreements in stromal segmentation and lymphocyte detection for the TILs scoring problem. The study emphasizes the potential impact of these measures in advancing histopathological image analysis, fostering consensus in TILs scoring, and ultimately improving breast cancer diagnostics and treatment planning. The source code and implementation guide for this study are accessible on our GitHub page, and the full clinical dataset is available for academic and research purposes on Kaggle.

Quantifying the relationship between cell proliferation and morphology during development of the face

Morphogenesis requires highly coordinated, complex interactions between cellular processes: proliferation, migration, and apoptosis, along with physical tissue interactions. How these cellular and tissue dynamics drive morphogenesis remains elusive. Three dimensional (3D) microscopic imaging poses great promise, and generates elegant images. However, generating even moderate through-put quantified images is challenging for many reasons. As a result, the association between morphogenesis and cellular processes in 3D developing tissues has not been fully explored. To address this critical gap, we have developed an imaging and image analysis pipeline to enable 3D quantification of cellular dynamics along with 3D morphology for the same individual embryo. Specifically, we focus on how 3D distribution of proliferation relates to morphogenesis during mouse facial development. Our method involves imaging with light-sheet microscopy, automated segmentation of cells and tissues using machine learning-based tools, and quantification of external morphology via geometric morphometrics. Applying this framework, we show that changes in proliferation are tightly correlated to changes in morphology over the course of facial morphogenesis. These analyses illustrate the potential of this pipeline to investigate mechanistic relationships between cellular dynamics and morphogenesis during embryonic development.