论文标题
3D医疗图像数据的汽车细分：2018年MSD挑战的贡献
AutoML Segmentation for 3D Medical Image Data: Contribution to the MSD Challenge 2018
论文作者
论文摘要
随着机器学习的最新进展，医学图像计算社区的语义细分领域取得了巨大进展。但是，开发算法通常仅根据一项任务来优化和验证。结合小数据集，解释结果的普遍性通常很困难。医学分割的十项全能挑战解决了这个问题，旨在促进不需要手动参数化的可推广的3D语义分割算法的开发。开发了这种算法，并在本文中介绍。它由一个3D卷积神经网络组成，该网络具有编码器架构，该架构采用了剩余连接，跳过连接和多级预测生成。它在各向异性体素地球分析中起作用，并具有各向异性深度，即，倒数步骤的数量是特定于任务的参数。在训练之前，为每个任务自动推断这些深度。通过将这种灵活的体系结构与在线数据增强和几乎没有预 - 或后处理的几乎没有的结果相结合，可以实现有希望的结果。为此挑战开发的代码将在最终截止日期之后在线提供：https：//github.com/orippler/msd_2018
Fueled by recent advances in machine learning, there has been tremendous progress in the field of semantic segmentation for the medical image computing community. However, developed algorithms are often optimized and validated by hand based on one task only. In combination with small datasets, interpreting the generalizability of the results is often difficult. The Medical Segmentation Decathlon challenge addresses this problem, and aims to facilitate development of generalizable 3D semantic segmentation algorithms that require no manual parametrization. Such an algorithm was developed and is presented in this paper. It consists of a 3D convolutional neural network with encoder-decoder architecture employing residual-connections, skip-connections and multi-level generation of predictions. It works on anisotropic voxel-geometries and has anisotropic depth, i.e., the number of downsampling steps is a task-specific parameter. These depths are automatically inferred for each task prior to training. By combining this flexible architecture with on-the-fly data augmentation and little-to-no pre-- or postprocessing, promising results could be achieved. The code developed for this challenge will be available online after the final deadline at: https://github.com/ORippler/MSD_2018
