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Softmax-Driven Active Shape Model for Segmenting Crowded Objects in Digital Pathology Images
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DOI：10.1109/ACCESS.2024.3369916.png)
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摘要
En 中文
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. In this work, 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 enables treatment of individual objects separately, while the segmentation map precisely outlines boundaries. 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. The proposed SD-ASM consistently outperformed reference methods, achieving up to 8.93% higher Aggregated Jaccard Index (AJI) and 9.84% increase in Panoptic Quality (PQ) score compared to segmentation networks alone. To emphasize versatility, we also applied SD-ASMs to segment hepatic steatosis and renal tubules, where individual structure identification is critical. Once again, integration of SD-ASM with deep models enhanced segmentation accuracy beyond prior works by up to 6.2% in AJI and 38% decrease in Hausdorff Distance. The proposed approach demonstrates effectiveness in accurately segmenting touching objects across multiple clinical scenarios.
Keyword:
Image segmentation
Instance segmentation
Deep learning
Active shape model
Task analysis
Image color analysis
Digital systems
Shape measurement
Pathology
Computer aided diagnosis
Histopathology
Microscopy
Biomedical imaging
Digital pathology
deep learning
hybrid frameworks
nuclei instance segmentation
active shape models
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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