Recent advances in deep learning, like 3D fully convolutional networks
(FCNs), have improved the state-of-the-art in dense semantic segmentation of
medical images. However, most network architectures require severely
downsampling or cropping the images to meet the memory limitations of today's
GPU cards while still considering enough context in the images for accurate
segmentation. In this work, we propose a novel approach that utilizes
auto-context to perform semantic segmentation at higher resolutions in a
multi-scale pyramid of stacked 3D FCNs. We train and validate our models on a
dataset of manually annotated abdominal organs and vessels from 377 clinical CT
images used in gastric surgery, and achieve promising results with close to 90%
Dice score on average. For additional evaluation, we perform separate testing
on datasets from different sources and achieve competitive results,
illustrating the robustness of the model and approach.

本文提出一种新的方法通过采用自动上下文来利用多尺度堆叠 3D FCN 金字塔，从而提高分辨率执行语义分割。 我们在手动注释的 377 个胃手术的 CT 影像数据集上训练和验证模型，并获得近 90％的 Dice 分数。

用于腹部多器官分割的多尺度金字塔 3D 全卷积网络

A multi-scale pyramid of 3D fully convolutional networks for abdominal  multi-organ segmentation

Recently deep residual learning with residual units for training very deep
neural networks advanced the state-of-the-art performance on 2D image
recognition tasks, e.g., object detection and segmentation. However, how to
fully leverage contextual representations for recognition tasks from volumetric
data has not been well studied, especially in the field of medical image
computing, where a majority of image modalities are in volumetric format. In
this paper we explore the deep residual learning on the task of volumetric
brain segmentation. There are at least two main contributions in our work.
First, we propose a deep voxelwise residual network, referred as VoxResNet,
which borrows the spirit of deep residual learning in 2D image recognition
tasks, and is extended into a 3D variant for handling volumetric data. Second,
an auto-context version of VoxResNet is proposed by seamlessly integrating the
low-level image appearance features, implicit shape information and high-level
context together for further improving the volumetric segmentation performance.
Extensive experiments on the challenging benchmark of brain segmentation from
magnetic resonance (MR) images corroborated the efficacy of our proposed method
in dealing with volumetric data. We believe this work unravels the potential of
3D deep learning to advance the recognition performance on volumetric image
segmentation.

本文介绍了在体积数据上使用基于深度残差学习的深度卷积网络 VoxResNet 用于脑部图像分割的任务，同时提出了一种通过无缝集成低阶图像外观特征、隐式形状信息和高级上下文的自动上下文版本 VoxResNet 来进一步提高体积分割性能的方法，实验证明该方法在处理体积数据方面表现出色，揭示出 3D 深度学习在体积图像分割中的潜力。