Deapartemnt of Computer Science and Engineering (AIML), Institute of Engineering and Management, Kolkata, West Bengal, India.
Deapartment of Computer Science and Engineering, Jadavpur University, Kolkata, West Bengal, India.
PLoS One. 2023 Jun 23;18(6):e0286862. doi: 10.1371/journal.pone.0286862. eCollection 2023.
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.
肿瘤微环境的鲁棒语义分割是机器学习赋能的计算病理学中的主要开放性挑战之一。尽管基于深度学习的系统已经取得了重大进展，但它们的任务不可知的数据驱动方法在生物医学应用中往往缺乏必要的上下文基础。我们提出了一种新颖的模糊水流方案，该方案利用基础深度学习框架的粗分割输出，然后提供更细粒度和实例级别的鲁棒分割输出。我们的两阶段协同分割方法 Deep-Fuzz 特别适用于重叠对象，并在四个公共细胞核分割数据集上实现了最先进的性能。我们还通过可视化示例展示了我们的最终输出如何更好地与病理见解保持一致，从而更具临床可解释性。
