From Cells to Functional Tissue Units: Scale-Aware Deep Learning for Biological Microscopy Segmentation

Author: Li, Qilin

Year: 2026

Degree: Dissertation (Ph.D.)

Advisor: Van Valen, David A.

Committee Members: Gkioxari, Georgia; Perona, Pietro; Yue, Yisong; Van Valen, David A.

Option: Electrical Engineering

Abstract

Biological microscopy turns cells, tissues and molecular labels into images, but quantitative biology depends on converting those images into reliable objects. Segmentation defines the cell boundaries, nuclei, functional tissue units (FTUs) and background regions from which morphology, spatial neighborhoods, tissue organization, and disease-associated microenvironments are measured. This step remains difficult because biological objects are dense, small, heterogeneous in appearance, expensive to annotate, and often embedded in gigapixel-scale images with substantial stain and scale variation. General computer-vision models provide useful architectures and representations, but they must be adapted before they can reliably support biological image analysis.

This thesis addresses these challenges through four connected contributions. First, it establishes a cell preprocessing and evaluation workflow including microscopy annotation conversion, tiling, normalization, small-object mask fidelity, and object-level evaluation. Second, it studies Equivariant Mask2Former, showing that finite rotation/reflection-equivariant feature processing can help in limited-label or weak-augmentation settings, but carries substantial computational cost. Third, it contributes to CellSAM, a detector-guided cell segmentation framework in which CellFinder supplies automatic bounding-box prompts for SAM-based mask decoding across diverse cellular imaging modalities. Fourth, it introduces FTURader, a scale-dynamic FTU segmentation pipeline that combines H&E stain normalization, FTUFinder detection, dynamic scale control, and SAM2-based mask decoding.

Together, these contributions bridge cell-scale and FTU-scale microscopy segmentation. They show that reliable biological segmentation depends on controlled data representation, metrics matched to the biological question, prompt generators adapted to dense microscopy, and scale-aware inference when the target changes from cells to anatomy. The resulting perspective is that segmentation should be designed and evaluated as a measurement system for specific biological units, not simply as a mask-prediction task.