Kumar Amish, Upadhyay Neha, Ghosal Palash, Chowdhury Tamal, Das Dipayan, Mukherjee Amritendu, Nandi Debashis
Department of Computer Science and Engineering, National Institute of Technoloy Durgapur - 713209, West Bengal, India.
Department of Electronics and Communication Engineering, National Institute of Technoloy Durgapur - 713209, West Bengal, India.
Comput Methods Programs Biomed. 2020 Sep;193:105524. doi: 10.1016/j.cmpb.2020.105524. Epub 2020 May 1.
Acute stroke lesion segmentation is of paramount importance as it can aid medical personnel to render a quicker diagnosis and administer consequent treatment. Automation of this task is technically exacting due to the variegated appearance of lesions and their dynamic development, medical discrepancies, unavailability of datasets, and the requirement of several MRI modalities for imaging. In this paper, we propose a composite deep learning model primarily based on the self-similar fractal networks and the U-Net model for performing acute stroke diagnosis tasks automatically to assist as well as expedite the decision-making process of medical practitioners.
We put forth a new deep learning architecture, the Classifier-Segmenter network (CSNet), involving a hybrid training strategy with a self-similar (fractal) U-Net model, explicitly designed to perform the task of segmentation. In fractal networks, the underlying design strategy is based on the repetitive generation of self-similar fractals in place of residual connections. The U-Net model exploits both spatial as well as semantic information along with parameter sharing for a faster and efficient training process. In this new architecture, we exploit the benefits of both by combining them into one hybrid training scheme and developing the concept of a cascaded architecture, which further enhances the model's accuracy by removing redundant parts from the Segmenter's input. Lastly, a voting mechanism has been employed to further enhance the overall segmentation accuracy.
The performance of the proposed architecture has been scrutinized against the existing state-of-the-art deep learning architectures applied to various biomedical image processing tasks by submission on the publicly accessible web platform provided by the MICCAI Ischemic Stroke Lesion Segmentation (ISLES) challenge. The experimental results demonstrate the superiority of the proposed method when compared to similar submitted strategies, both qualitatively and quantitatively in terms of some of the well known evaluation metrics, such as Accuracy, Dice-Coefficient, Recall, and Precision.
We believe that our method may find use as a handy tool for doctors to identify the location and extent of irreversibly damaged brain tissue, which is said to be a critical part of the decision-making process in case of an acute stroke.
急性中风病灶分割至关重要，因为它能帮助医护人员更快地做出诊断并进行后续治疗。由于病灶外观多样、动态发展、医学差异、数据集不可用以及成像需要多种磁共振成像（MRI）模态，该任务的自动化在技术上要求很高。在本文中，我们提出一种主要基于自相似分形网络和U-Net模型的复合深度学习模型，用于自动执行急性中风诊断任务，以协助并加快医生的决策过程。
我们提出一种新的深度学习架构，即分类器-分割器网络（CSNet），它涉及一种与自相似（分形）U-Net模型的混合训练策略，专门设计用于执行分割任务。在分形网络中，基本设计策略是基于自相似分形的重复生成来替代残差连接。U-Net模型利用空间和语义信息以及参数共享来实现更快且高效的训练过程。在这种新架构中，我们通过将两者结合到一个混合训练方案中，并开发级联架构的概念来利用两者的优势，通过从分割器输入中去除冗余部分进一步提高模型的准确性。最后，采用投票机制进一步提高整体分割准确性。
通过在医学图像计算与计算机辅助干预国际会议（MICCAI）缺血性中风病灶分割（ISLES）挑战赛提供的公共网络平台上提交结果，将所提出架构的性能与应用于各种生物医学图像处理任务的现有先进深度学习架构进行了对比。实验结果表明，与类似的提交策略相比，所提出的方法在准确性、骰子系数、召回率和精确率等一些知名评估指标方面，在定性和定量上都具有优越性。
我们相信我们的方法可能会成为医生识别不可逆损伤脑组织位置和范围的便捷工具，而这在急性中风情况下是决策过程的关键部分。
