A Multi-Modal Pelvic MRI Dataset for Deep Learning-Based Pelvic Organ Segmentation in Endometriosis.
Authors
Affiliations (6)
Affiliations (6)
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, USA.
- The Department of Obstetrics and Gynecology, Baylor College of Medicine, Houston, USA.
- Texas Children's Hospital Pavilion for Women, Houston, USA.
- McGovern Medical School, University of Texas Health Science Center at Houston, Houston, USA.
- Department of Diagnostic and Interventional Imaging, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, USA.
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, USA. [email protected].
Abstract
Endometriosis affects approximately 190 million females of reproductive age worldwide. Magnetic Resonance Imaging (MRI) has been recommended as the primary non-invasive diagnostic method for endometriosis. This study presents new female pelvic MRI multicenter datasets for endometriosis and shows the baseline segmentation performance of two auto-segmentation pipelines: the self-configuring nnU-Net and RAovSeg, a custom network. The multi-sequence endometriosis MRI scans from two clinical institutions were collected. A multicenter dataset of 51 subjects with manual labels for multiple pelvic structures from three raters was used to assess interrater agreement. A second single-center dataset of 81 subjects with labels for multiple pelvic structures from one rater was used to develop the ovary auto-segmentation pipelines. Uterus and ovary segmentations are available for all subjects, endometrioma segmentation is available for all subjects where it is detectable in the image. This study highlights the challenges of manual ovary segmentation in endometriosis MRI and emphasizes the need for an auto-segmentation method. The dataset is publicly available for further research in pelvic MRI auto-segmentation to support endometriosis research.