Autoencoders for Multi-Label Prostate MR Segmentation

Abstract

Organ image segmentation can be improved by implementing prior knowledge about the anatomy. One way of doing this is by training an autoencoder to learn a lowdimensional representation of the segmentation. In this paper, this is applied in multi-label prostate MR segmentation, with some positive results.

References in corpus (1)

• Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation

Cited by in corpus (2)

• Automatic Prostate Zonal Segmentation Using Fully Convolutional Network with Feature Pyramid Attention
• Effect of Adding Probabilistic Zonal Prior in Deep Learning-based Prostate Cancer Detection