Comparing multi-image and image augmentation strategies for deep learning-based prostate segmentation

TL;DR

This study compares using multiple images versus image augmentation for training deep learning models to segment the prostate in radiotherapy, finding that standard augmentation methods are more effective.

Contribution

The paper evaluates the effectiveness of multi-image strategies versus standard augmentation for prostate segmentation in low-data scenarios.

Findings

Using multiple images showed minimal improvement in DICE and Hausdorff 95% metrics compared to single images.

The rectum showed the maximum difference in performance when training with images from five patients.

Standard augmentation methods outperformed multi-image strategies in most cases.

Abstract

During MR-Linac-based adaptive radiotherapy, multiple images are acquired per patient. These can be applied in training deep learning networks to reduce annotation efforts. This study examined the advantage of using multiple versus single images for prostate treatment segmentation. Findings indicate minimal improvement in DICE and Hausdorff 95% metrics with multiple images. Maximum difference was seen for the rectum in the low data regime, training with images from five patients. Utilizing a 2D U-net resulted in DICE values of 0.80/0.83 when including 1/5 images per patient, respectively. Including more patients in training reduced the difference. Standard augmentation methods remained more effective.

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