Adversarial Networks for the Detection of Aggressive Prostate Cancer

TL;DR

This paper introduces an adversarial training approach for semantic segmentation of aggressive prostate cancer in MRI images, improving detection sensitivity and accuracy especially on small datasets.

Contribution

The novel use of an adversarial network to enhance global segmentation quality in medical imaging, particularly effective for small, complex datasets.

Findings

Improved detection sensitivity for aggressive prostate cancer.

Higher dice-score compared to standard methods.

Particularly effective on small datasets.

Abstract

Semantic segmentation constitutes an integral part of medical image analyses for which breakthroughs in the field of deep learning were of high relevance. The large number of trainable parameters of deep neural networks however renders them inherently data hungry, a characteristic that heavily challenges the medical imaging community. Though interestingly, with the de facto standard training of fully convolutional networks (FCNs) for semantic segmentation being agnostic towards the `structure' of the predicted label maps, valuable complementary information about the global quality of the segmentation lies idle. In order to tap into this potential, we propose utilizing an adversarial network which discriminates between expert and generated annotations in order to train FCNs for semantic segmentation. Because the adversary constitutes a learned parametrization of what makes a good segmentation at a global level, we hypothesize that the method holds particular advantages for segmentation tasks on complex structured, small datasets. This holds true in our experiments: We learn to segment aggressive prostate cancer utilizing MRI images of 152 patients and show that the proposed scheme is superior over the de facto standard in terms of the detection sensitivity and the dice-score for aggressive prostate cancer. The achieved relative gains are shown to be particularly pronounced in the small dataset limit.