简单的MyUnet3D BraTS分割

Agus Subhan Akbar, C. Fatichah, N. Suciati
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引用次数: 3

摘要

在BraTS挑战中用于脑肿瘤分割的深度学习架构在WT、TC和ET分割中表现良好。然而,这些体系结构通常有许多参数,并且需要模型的大存储容量。在本文中,我们提出了一个简单的MyUnet3D对BraTS 2018数据集进行分割。该架构受到二维U-Net的启发,并对其进行了改进,以进行三维图像分割。数据集分为两部分,一部分用于训练,另一部分用于验证。285个数据中,213个用于训练,72个用于验证模型。该分割由肿瘤整体(WT)、肿瘤核心(TC)和增强肿瘤(ET) 3部分组成。即使它很简单,它在分割整个肿瘤时产生的骰子系数为0.8269。然而,其在肿瘤核心和增强肿瘤中的表现有待进一步发展。肿瘤的简单性及其对整个肿瘤的分割效果有很大的发展潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Simple MyUnet3D for BraTS Segmentation
The deep learning architectures that have been used for brain tumor segmentation in the BraTS challenge have performed well for the WT, TC, and ET segmentations. However, these architectures generally have many parameters and require large storage capacity for the model. In this paper, we propose a Simple MyUnet3D to do segmentation on BraTS 2018 dataset. This proposed architecture was inspired by 2D U-Net and modified to do 3D image segmentation. Dataset divides into 2 parts, one part of training and the other for validation. From 285 data, 213 for training, and 72 for validating the model. The segmentation consists of 3 parts, whole tumor(WT), tumor core(TC), and enhanced tumor(ET). Even its simplicity, it produces a dice coefficient of 0.8269 at segmenting the whole tumor. Nevertheless, its performance in tumor core and enhanced tumor need to be developed. The simplicity and its result in segmenting the whole tumor have great potential to be better developed.
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