论文标题
从3D MRI的脑肿瘤区域的稳健语义分割
Robust Semantic Segmentation of Brain Tumor Regions from 3D MRIs
论文作者
论文摘要
多模式脑肿瘤分割挑战（BRAT）将研究人员汇集在一起，以改善3D MRI脑肿瘤分割的自动化方法。肿瘤分割是诊断和治疗疾病计划所必需的基本视觉任务之一。由于现代GPU的出现，前几年获胜的方法都是基于深度学习的，这些方法可以快速优化深卷积神经网络体系结构。在这项工作中，我们探讨了3D语义细分的最佳实践，包括常规编码器架构以及合并的损失功能，以进一步提高细分精度。我们评估了Brats 2019挑战的方法。
Multimodal brain tumor segmentation challenge (BraTS) brings together researchers to improve automated methods for 3D MRI brain tumor segmentation. Tumor segmentation is one of the fundamental vision tasks necessary for diagnosis and treatment planning of the disease. Previous years winning methods were all deep-learning based, thanks to the advent of modern GPUs, which allow fast optimization of deep convolutional neural network architectures. In this work, we explore best practices of 3D semantic segmentation, including conventional encoder-decoder architecture, as well combined loss functions, in attempt to further improve the segmentation accuracy. We evaluate the method on BraTS 2019 challenge.
