SD-ASM dataset and script

This repository contains the codes and the script of the work: "Softmax-driven active shape model for segmenting crowded objects in digital pathology images " IEEE Access, 2024

Abstract:
Background: Automated segmentation of histological structures in microscopy images is a crucial step in computer-aided diagnosis framework. However, this task remains a challenging problem due to issues like overlapping and touching objects, shape variation, and background complexity.
Methods: To address this challenge, we present a novel and effective approach for instance segmentation through the synergistic combination of two deep learning networks (detection and segmentation models) with active shape models. Our method, called softmax-driven active shape model (SD-ASM), uses information from deep neural networks to initialize and evolve a dynamic deformable model. The detection module of the deep network enables the treatment of individual objects separately, while the semantic segmentation map precisely outlines the boundaries of histological structures.
Results: Our experiments demonstrate that integrating SD-ASMs into deep learning frameworks significantly enhances instance segmentation performance. We conducted extensive tests using various state-of-the-art architectures on two standard datasets for segmenting crowded objects like cell nuclei - MoNuSeg and CoNIC. To emphasize the versatility of our approach, we applied SD-ASMs in two additional clinical cases: steatosis and tubules segmentation. Once again, the integration of SD-ASM consistently outperformed reference methods, highlighting its effectiveness in accurately segmenting touching objects across multiple clinical scenarios.
Conclusions: The proposed approach is inherently generalizable and can be easily applied to other imaging modalities and crowded object segmentation tasks, including fluorescence cellular imaging.