This dataset introduces a Multi-Organ Gynaecological Magnetic Resonance Imaging for Brachytherapy-based Oncology (MOGaMBO) dataset, a novel magnetic resonance imaging dataset aimed to advance research in applications of computational intelligence in brachytherapy diagnosis and organ segmentation for cervical cancer treatment. The dataset comprises high-resolution T2-weighted 3D MR scans from 94 patients with locally advanced cervical cancer (stages IB2–IVA), adhering to FIGO guidelines for interstitial and intra-cavitary brachytherapy. The imaging was performed using a 1.5T GE Signa Explorer scanner, with acquisition parameters TR and TE set to optimal values for soft-tissue contrast at 2600ms and 155ms, respectively, combined with a pixel resolution of 0.5 × 0.5 mm² and 30–50 slices per scan. To ensure dosimetric consistency, bladder volume was standardized via Foley catheterization during imaging. The critical organs-at-risk—urinary bladder, rectum, sigmoid colon, and femoral heads, were manually contoured by expert radiation oncologists using the open-source ITK-SNAP platform, ensuring precise region-of-interest annotations. The dataset underwent rigorous deidentification to protect patient privacy, removing all demographic and identifiable information. MOGaMBO provides a standardized, privacy-compliant resource for developing and validating medical image segmentation or representation learning algorithms, and brachytherapy-related research tools. This dataset addresses a critical gap in accessible, multi-organ imaging resources for the gynaecological brachytherapy dataset, with applications in treatment planning, and AI-driven clinical research.

If you use this dataset, then cite the following work in the appropriate format

BibTex:

@misc{manna2025,
      title={Federated Self-Supervised Learning for One-Shot Cross-Modal and Cross-Imaging Technique Segmentation}, 
      author={Siladittya Manna and Suresh Das and Sayantari Ghosh and Saumik Bhattacharya},
      year={2025},
      eprint={2503.23507},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2503.23507}, 
}

MLA:
Manna, Siladittya, et al. "Federated Self-Supervised Learning for One-Shot Cross-Modal and Cross-Imaging Technique Segmentation." arXiv preprint arXiv:2503.23507 (2025).

APA:
Manna, S., Das, S., Ghosh, S., & Bhattacharya, S. (2025). Federated Self-Supervised Learning for One-Shot Cross-Modal and Cross-Imaging Technique Segmentation. arXiv preprint arXiv:2503.23507.

Chicago:
Manna, Siladittya, Suresh Das, Sayantari Ghosh, and Saumik Bhattacharya. "Federated Self-Supervised Learning for One-Shot Cross-Modal and Cross-Imaging Technique Segmentation." arXiv preprint arXiv:2503.23507 (2025).

Harvard:
Manna, S., Das, S., Ghosh, S. and Bhattacharya, S., 2025. Federated Self-Supervised Learning for One-Shot Cross-Modal and Cross-Imaging Technique Segmentation. arXiv preprint arXiv:2503.23507.