A Multi-Modal Pelvic MRI Dataset for Deep Learning-Based Pelvic Organ Segmentation in Endometriosis
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...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05623-3 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849387918346420224 |
|---|---|
| author | Xiaomin Liang Linda A. Alpuing Radilla Kamand Khalaj Haaniya Dawoodally Chinmay Mokashi Xiaoming Guan Kirk E. Roberts Sunil A. Sheth Varaha S. Tammisetti Luca Giancardo |
| author_facet | Xiaomin Liang Linda A. Alpuing Radilla Kamand Khalaj Haaniya Dawoodally Chinmay Mokashi Xiaoming Guan Kirk E. Roberts Sunil A. Sheth Varaha S. Tammisetti Luca Giancardo |
| author_sort | Xiaomin Liang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8babd44ebd6e4febade391048e911272 |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-8babd44ebd6e4febade391048e9112722025-08-20T03:42:26ZengNature PortfolioScientific Data2052-44632025-07-011211810.1038/s41597-025-05623-3A Multi-Modal Pelvic MRI Dataset for Deep Learning-Based Pelvic Organ Segmentation in EndometriosisXiaomin Liang0Linda A. Alpuing Radilla1Kamand Khalaj2Haaniya Dawoodally3Chinmay Mokashi4Xiaoming Guan5Kirk E. Roberts6Sunil A. Sheth7Varaha S. Tammisetti8Luca Giancardo9McWilliams School of Biomedical Informatics, University of Texas Health Science Center at HoustonThe Department of Obstetrics and Gynecology, Baylor College of MedicineMcWilliams School of Biomedical Informatics, University of Texas Health Science Center at HoustonMcWilliams School of Biomedical Informatics, University of Texas Health Science Center at HoustonMcWilliams School of Biomedical Informatics, University of Texas Health Science Center at HoustonThe Department of Obstetrics and Gynecology, Baylor College of MedicineMcWilliams School of Biomedical Informatics, University of Texas Health Science Center at HoustonMcGovern Medical School, University of Texas Health Science Center at HoustonDepartment of Diagnostic and Interventional Imaging, McGovern Medical School, University of Texas Health Science Center at HoustonMcWilliams School of Biomedical Informatics, University of Texas Health Science Center at HoustonAbstract 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.https://doi.org/10.1038/s41597-025-05623-3 |
| spellingShingle | Xiaomin Liang Linda A. Alpuing Radilla Kamand Khalaj Haaniya Dawoodally Chinmay Mokashi Xiaoming Guan Kirk E. Roberts Sunil A. Sheth Varaha S. Tammisetti Luca Giancardo A Multi-Modal Pelvic MRI Dataset for Deep Learning-Based Pelvic Organ Segmentation in Endometriosis Scientific Data |
| title | A Multi-Modal Pelvic MRI Dataset for Deep Learning-Based Pelvic Organ Segmentation in Endometriosis |
| title_full | A Multi-Modal Pelvic MRI Dataset for Deep Learning-Based Pelvic Organ Segmentation in Endometriosis |
| title_fullStr | A Multi-Modal Pelvic MRI Dataset for Deep Learning-Based Pelvic Organ Segmentation in Endometriosis |
| title_full_unstemmed | A Multi-Modal Pelvic MRI Dataset for Deep Learning-Based Pelvic Organ Segmentation in Endometriosis |
| title_short | A Multi-Modal Pelvic MRI Dataset for Deep Learning-Based Pelvic Organ Segmentation in Endometriosis |
| title_sort | multi modal pelvic mri dataset for deep learning based pelvic organ segmentation in endometriosis |
| url | https://doi.org/10.1038/s41597-025-05623-3 |
| work_keys_str_mv | AT xiaominliang amultimodalpelvicmridatasetfordeeplearningbasedpelvicorgansegmentationinendometriosis AT lindaaalpuingradilla amultimodalpelvicmridatasetfordeeplearningbasedpelvicorgansegmentationinendometriosis AT kamandkhalaj amultimodalpelvicmridatasetfordeeplearningbasedpelvicorgansegmentationinendometriosis AT haaniyadawoodally amultimodalpelvicmridatasetfordeeplearningbasedpelvicorgansegmentationinendometriosis AT chinmaymokashi amultimodalpelvicmridatasetfordeeplearningbasedpelvicorgansegmentationinendometriosis AT xiaomingguan amultimodalpelvicmridatasetfordeeplearningbasedpelvicorgansegmentationinendometriosis AT kirkeroberts amultimodalpelvicmridatasetfordeeplearningbasedpelvicorgansegmentationinendometriosis AT sunilasheth amultimodalpelvicmridatasetfordeeplearningbasedpelvicorgansegmentationinendometriosis AT varahastammisetti amultimodalpelvicmridatasetfordeeplearningbasedpelvicorgansegmentationinendometriosis AT lucagiancardo amultimodalpelvicmridatasetfordeeplearningbasedpelvicorgansegmentationinendometriosis AT xiaominliang multimodalpelvicmridatasetfordeeplearningbasedpelvicorgansegmentationinendometriosis AT lindaaalpuingradilla multimodalpelvicmridatasetfordeeplearningbasedpelvicorgansegmentationinendometriosis AT kamandkhalaj multimodalpelvicmridatasetfordeeplearningbasedpelvicorgansegmentationinendometriosis AT haaniyadawoodally multimodalpelvicmridatasetfordeeplearningbasedpelvicorgansegmentationinendometriosis AT chinmaymokashi multimodalpelvicmridatasetfordeeplearningbasedpelvicorgansegmentationinendometriosis AT xiaomingguan multimodalpelvicmridatasetfordeeplearningbasedpelvicorgansegmentationinendometriosis AT kirkeroberts multimodalpelvicmridatasetfordeeplearningbasedpelvicorgansegmentationinendometriosis AT sunilasheth multimodalpelvicmridatasetfordeeplearningbasedpelvicorgansegmentationinendometriosis AT varahastammisetti multimodalpelvicmridatasetfordeeplearningbasedpelvicorgansegmentationinendometriosis AT lucagiancardo multimodalpelvicmridatasetfordeeplearningbasedpelvicorgansegmentationinendometriosis |