Deep-Fuzz: A synergistic integration of deep learning and fuzzy water flows for fine-grained nuclei segmentation in digital pathology

Robust semantic segmentation of tumour micro-environment is one of the major open challenges in machine learning enabled computational pathology. Though deep learning based systems have made significant progress, their task agnostic data driven approach often lacks the contextual grounding necessary...

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Bibliographic Details
Published in:PloS one 2023-06, Vol.18 (6), p.e0286862-e0286862
Main Authors: Das, Nirmal, Saha, Satadal, Nasipuri, Mita, Basu, Subhadip, Chakraborti, Tapabrata
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Biomedical engineering
Biomedical materials
Cell Nucleus
Computer and Information Sciences
Deep Learning
Design
Hydraulic measurements
Image Processing, Computer-Assisted
Machine Learning
Medicine and Health Sciences
Nuclei (cytology)
Pathology
People and Places
Physical Sciences
Research and Analysis Methods
Semantic segmentation
Vision systems
Water
Water flow
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Summary:Robust semantic segmentation of tumour micro-environment is one of the major open challenges in machine learning enabled computational pathology. Though deep learning based systems have made significant progress, their task agnostic data driven approach often lacks the contextual grounding necessary in biomedical applications. We present a novel fuzzy water flow scheme that takes the coarse segmentation output of a base deep learning framework to then provide a more fine-grained and instance level robust segmentation output. Our two stage synergistic segmentation method, Deep-Fuzz, works especially well for overlapping objects, and achieves state-of-the-art performance in four public cell nuclei segmentation datasets. We also show through visual examples how our final output is better aligned with pathological insights, and thus more clinically interpretable.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0286862