Abstract

Segmentation of a brain tumor from magnetic resonance multimodal images is a challenging task in the field of medical imaging. The vast diversity in potential target regions, appearance and multifarious intensity threshold levels of various tumor types are few of the major factors that affect segmentation results. An accurate diagnosis and its treatment demand strict delineation of the tumor affected tissues. Herein, we focus on a smart, automated, and robust segmentation approach for brain tumor using a modified 3D U-Net architecture. The pre-operative multimodal 3D-MRI scans of High-Grade Glioma (HGG) and Low-Grade Glioma (LGG) are used as data. Our proposed approach solves the problem of memory and system resource constraints by robustly applying dense network training on image patches of 3D volumes. It improves the border region artifact detection by applying convolutions at an appropriate phase in the proposed neural network. Multi-class imbalance data are handled by using Categorical Cross Entropy (CCE) loss developed by combining the Weighted Cross Entropy (WCE) with Weighted Multi-class Dice Loss (WMDL) functions, which enables the network to perform smart segmentation of the smaller tumorous regions. The proposed approach is tested and evaluated for the challenge datasets of multimodal MRI volumes of tumor patients. Experiments are performed to compute the average dice scores on BraTS-2019 and BraTS-2020 datasets for the whole tumor region.

摘要

从磁共振多峰图像分割脑肿瘤是医学成像领域的一项艰巨任务。潜在的目标区域，外观和各种肿瘤类型的多种强度阈值水平的巨大差异是影响分割结果的主要因素很少。准确的诊断及其治疗要求严格描述受肿瘤影响的组织。本文中，我们重点介绍了使用改良的3D U-Net架构对脑肿瘤进行智能，自动化和鲁棒分割的方法。高度胶质瘤（HGG）和低度胶质瘤（LGG）的术前多峰3D-MRI扫描用作数据。我们提出的方法通过在3D卷的图像块上稳健地应用密集网络训练来解决内存和系统资源约束的问题。通过在建议的神经网络中的适当阶段应用卷积，可以改善边界区域伪影的检测。多类别不平衡数据是通过使用加权交叉熵（WCE）与加权多类别骰子损失（WMDL）函数结合开发的分类交叉熵（CCE）损失来处理的，该分类使网络能够对较小的肿瘤进行智能分割地区。测试和评估了所提出的方法，以评估肿瘤患者多模式MRI量的挑战数据集。进行实验以计算BraTS-2019和BraTS-2020数据集上整个肿瘤区域的平均骰子得分。多类别不平衡数据是通过使用加权交叉熵（WCE）与加权多类别骰子损失（WMDL）函数结合开发的分类交叉熵（CCE）损失来处理的，该分类使网络能够对较小的肿瘤进行智能分割地区。测试和评估了所提出的方法，以评估肿瘤患者多模式MRI量的挑战数据集。进行实验以计算BraTS-2019和BraTS-2020数据集上整个肿瘤区域的平均骰子得分。多类别不平衡数据是通过使用加权交叉熵（WCE）与加权多类别骰子损失（WMDL）函数结合开发的分类交叉熵（CCE）损失来处理的，该分类使网络能够对较小的肿瘤进行智能分割地区。测试和评估了所提出的方法，以评估肿瘤患者多模式MRI量的挑战数据集。进行实验以计算BraTS-2019和BraTS-2020数据集上整个肿瘤区域的平均骰子得分。