论文标题
具有多降降低输入的多二次编码网络用于肿瘤分割
Multi-Decoder Networks with Multi-Denoising Inputs for Tumor Segmentation
论文作者
论文摘要
从多模式MRI扫描中自动分割脑神经胶质瘤在临床试验和实践中起关键作用。不幸的是，尽管人类注释的差异很大和不确定性，但手动细分非常具有挑战性，耗时，昂贵且常常不准确。在当前的工作中，我们通过使用部分共享的编码共同学习三个单独的子问题，使用多码编码器体系结构开发了一种基于端到端的深度学习分割方法。我们还建议将平滑方法应用于输入映像，以生成DeNo的版本作为网络的其他输入。验证性能表明使用提出的方法时有所改进。在多模式磁共振成像挑战2020年，脑肿瘤中分割的不确定性定量的任务中，所提出的方法排名第二。
Automatic segmentation of brain glioma from multimodal MRI scans plays a key role in clinical trials and practice. Unfortunately, manual segmentation is very challenging, time-consuming, costly, and often inaccurate despite human expertise due to the high variance and high uncertainty in the human annotations. In the present work, we develop an end-to-end deep-learning-based segmentation method using a multi-decoder architecture by jointly learning three separate sub-problems using a partly shared encoder. We also propose to apply smoothing methods to the input images to generate denoised versions as additional inputs to the network. The validation performance indicate an improvement when using the proposed method. The proposed method was ranked 2nd in the task of Quantification of Uncertainty in Segmentation in the Brain Tumors in Multimodal Magnetic Resonance Imaging Challenge 2020.
