Huang He, Yang Guang, Zhang Wenbo, Xu Xiaomei, Yang Weiji, Jiang Weiwei, Lai Xiaobo
College of Medical Technology, Zhejiang Chinese Medical University, Hangzhou, China.
Cardiovascular Research Centre, Royal Brompton Hospital, London, United Kingdom.
Front Oncol. 2021 Jun 4;11:690244. doi: 10.3389/fonc.2021.690244. eCollection 2021.
Glioma is the most common primary central nervous system tumor, accounting for about half of all intracranial primary tumors. As a non-invasive examination method, MRI has an extremely important guiding role in the clinical intervention of tumors. However, manually segmenting brain tumors from MRI requires a lot of time and energy for doctors, which affects the implementation of follow-up diagnosis and treatment plans. With the development of deep learning, medical image segmentation is gradually automated. However, brain tumors are easily confused with strokes and serious imbalances between classes make brain tumor segmentation one of the most difficult tasks in MRI segmentation. In order to solve these problems, we propose a deep multi-task learning framework and integrate a multi-depth fusion module in the framework to accurately segment brain tumors. In this framework, we have added a distance transform decoder based on the V-Net, which can make the segmentation contour generated by the mask decoder more accurate and reduce the generation of rough boundaries. In order to combine the different tasks of the two decoders, we weighted and added their corresponding loss functions, where the distance map prediction regularized the mask prediction. At the same time, the multi-depth fusion module in the encoder can enhance the ability of the network to extract features. The accuracy of the model will be evaluated online using the multispectral MRI records of the BraTS 2018, BraTS 2019, and BraTS 2020 datasets. This method obtains high-quality segmentation results, and the average Dice is as high as 78%. The experimental results show that this model has great potential in segmenting brain tumors automatically and accurately.
胶质瘤是最常见的原发性中枢神经系统肿瘤，约占所有颅内原发性肿瘤的一半。作为一种非侵入性检查方法，磁共振成像（MRI）在肿瘤的临床干预中具有极其重要的指导作用。然而，医生手动从MRI中分割脑肿瘤需要耗费大量时间和精力，这影响了后续诊断和治疗计划的实施。随着深度学习的发展，医学图像分割逐渐实现自动化。然而，脑肿瘤容易与中风混淆，并且类别之间严重失衡使得脑肿瘤分割成为MRI分割中最困难的任务之一。为了解决这些问题，我们提出了一种深度多任务学习框架，并在该框架中集成了一个多深度融合模块，以准确分割脑肿瘤。在这个框架中，我们在V-Net的基础上添加了一个距离变换解码器，它可以使掩码解码器生成的分割轮廓更加准确，并减少粗糙边界的产生。为了结合两个解码器的不同任务，我们对它们相应的损失函数进行加权并相加，其中距离图预测对掩码预测进行正则化。同时，编码器中的多深度融合模块可以增强网络提取特征的能力。将使用BraTS 2018、BraTS 2019和BraTS 2020数据集的多光谱MRI记录在线评估模型的准确性。该方法获得了高质量的分割结果，平均Dice系数高达78%。实验结果表明，该模型在自动、准确地分割脑肿瘤方面具有很大潜力。
