A Multi-Stage Auto-Context Deep Learning Framework for Tissue and Nuclei Segmentation and Classification in H&E-Stained Histological Images of Advanced Melanoma
This addresses the need for more accurate diagnosis and treatment planning for melanoma patients by improving automated analysis, though it is incremental as it builds on existing auto-context methods.
The paper tackled the problem of segmenting and classifying tissues and nuclei in melanoma histological images by proposing a multi-stage deep learning framework that integrates both tasks, achieving second and first place in the PUMA challenge with scores of 73.40% and 63.48%.
Melanoma is the most lethal form of skin cancer, with an increasing incidence rate worldwide. Analyzing histological images of melanoma by localizing and classifying tissues and cell nuclei is considered the gold standard method for diagnosis and treatment options for patients. While many computerized approaches have been proposed for automatic analysis, most perform tissue-based analysis and nuclei (cell)-based analysis as separate tasks, which might be suboptimal. In this work, using the PUMA challenge dataset, we propose a novel multi-stage deep learning approach by combining tissue and nuclei information in a unified framework based on the auto-context concept to perform segmentation and classification in histological images of melanoma. Through pre-training and further post-processing, our approach achieved second and first place rankings in the PUMA challenge, with average micro Dice tissue score and summed nuclei F1-score of 73.40% for Track 1 and 63.48% for Track 2, respectively. Furthermore, through a comprehensive ablation study and additional evaluation on an external dataset, we demonstrated the effectiveness of the framework components as well as the generalization capabilities of the proposed approach. Our implementation for training and testing is available at: https://github.com/NimaTorbati/PumaSubmit