In this work, we propose to resolve the issue existing in current deep
learning based organ segmentation systems that they often produce results that
do not capture the overall shape of the target organ and often lack smoothness.
Since there is a rigorous mapping between the Signed Distance Map (SDM)
calculated from object boundary contours and the binary segmentation map, we
exploit the feasibility of learning the SDM directly from medical scans. By
converting the segmentation task into predicting an SDM, we show that our
proposed method retains superior segmentation performance and has better
smoothness and continuity in shape. To leverage the complementary information
in traditional segmentation training, we introduce an approximated Heaviside
function to train the model by predicting SDMs and segmentation maps
simultaneously. We validate our proposed models by conducting extensive
experiments on a hippocampus segmentation dataset and the public MICCAI 2015
Head and Neck Auto Segmentation Challenge dataset with multiple organs. While
our carefully designed backbone 3D segmentation network improves the Dice
coefficient by more than 5% compared to current state-of-the-arts, the proposed
model with SDM learning produces smoother segmentation results with smaller
Hausdorff distance and average surface distance, thus proving the effectiveness
of our method.

该研究使用 Signed Distance Map 直接从医学扫描中学习目标器官的形态，实现了更好的器官分割性能和更好的平滑度。通过同时预测 SDM 和分割地图，该方法将传统分割训练信息的补充信息引入，验证表明其比现有技术提高了 5% 以上的 Dice 系数并产生了更平滑的分割结果。

通过预测符号距离图进行形状感知的器官分割

Shape-Aware Organ Segmentation by Predicting Signed Distance Maps

The computer-aided analysis of medical scans is a longstanding goal in the
medical imaging field. Currently, deep learning has became a dominant
methodology for supporting pathologists and radiologist. Deep learning
algorithms have been successfully applied to digital pathology and radiology,
nevertheless, there are still practical issues that prevent these tools to be
widely used in practice. The main obstacles are low number of available cases
and large size of images (a.k.a. the small n, large p problem in machine
learning), and a very limited access to annotation at a pixel level that can
lead to severe overfitting and large computational requirements. We propose to
handle these issues by introducing a framework that processes a medical image
as a collection of small patches using a single, shared neural network. The
final diagnosis is provided by combining scores of individual patches using a
permutation-invariant operator (combination). In machine learning community
such approach is called a multi-instance learning (MIL).

该论文提出了一种处理医学影像的框架，使用多实例学习多个小图像块的神经网络得出最终诊断，以解决机器学习中小 n，大 p 问题和像素级注释的限制。