Boundary-Aware Instance Segmentation in Microscopy Imaging
This work addresses a persistent problem in cellular dynamics research by enhancing instance segmentation for microscopy imaging, though it appears incremental as it builds on existing foundation models like SAM.
The paper tackles the challenge of separating touching or overlapping cells in microscopy videos by proposing a boundary-aware instance segmentation framework that predicts signed distance functions, resulting in improved boundary accuracy and instance-level performance on public and private datasets.
Accurate delineation of individual cells in microscopy videos is essential for studying cellular dynamics, yet separating touching or overlapping instances remains a persistent challenge. Although foundation-model for segmentation such as SAM have broadened the accessibility of image segmentation, they still struggle to separate nearby cell instances in dense microscopy scenes without extensive prompting. We propose a prompt-free, boundary-aware instance segmentation framework that predicts signed distance functions (SDFs) instead of binary masks, enabling smooth and geometry-consistent modeling of cell contours. A learned sigmoid mapping converts SDFs into probability maps, yielding sharp boundary localization and robust separation of adjacent instances. Training is guided by a unified Modified Hausdorff Distance (MHD) loss that integrates region- and boundary-based terms. Evaluations on both public and private high-throughput microscopy datasets demonstrate improved boundary accuracy and instance-level performance compared to recent SAM-based and foundation-model approaches. Source code is available at: https://github.com/ThomasMendelson/BAISeg.git