Preprocessing Evaluation and Benchmark for Multi-structure Segmentation of the Male Pelvis in MRI on the Gold Atlas Dataset

Abstract

In radiation therapy (RTx), an accurate delineation of the regions of interest and organs at risk allows for a more targeted irradiation with reduced side effects. In the case of prostate cancer treatments, RTx planning requires the delineation of many pelvic structures. This is a time-consuming task and clinicians would greatly benefit from using robust automatic multi-structure segmentation tools.With the final purpose of introducing an automatic segmentation algorithm in clinical practice, we first address the problem of multi-structure segmentation in pelvic MR using a publicly available dataset. Moreover, we evaluate three types of preprocessing approaches to enable training and inference using different MR sequences. Despite a limited number of training samples, we report an average Dice score of 84.7 ± 10.2% in the segmentation of 8 pelvic structures. The code and the trained models are available at: https://github.com/FrancescaDB/multi_structure_segmentation_gold_atlas