{
  "abstract": "Background: Deep learning has significantly improved medical image segmentation, particularly in complex applications such as gynecological brachytherapy (BT). This study proposes a novel U-Net variant, the residual in residual dense block U-Net (RU-Net), to address the segmentation of organs-at-risk (OARs), which are often distorted due to BT probe insertion. Accurate segmentation is essential for effective treatment planning; however, task-specific datasets are scarce. To tackle this, a dedicated magnetic resonance imaging (MRI) dataset was developed, focusing on the precise delineation of OARs. Methods: The RU-Net architecture enhances feature extraction through embedded residual in residual dence block (RRDB) modules. It was trained and evaluated using a multiplanar (axial, coronal, and sagittal) MRI dataset. The study benchmarks RU-Net against pyramid scene parsing network, residual U-Net, U-Net++, and traditional U-Net models. Performance was assessed using categorical cross-entropy and dice loss metrics. Results: Under the dice loss function, RU-Net consistently outperformed competing models across all anatomical planes. It achieved superior scores in accuracy, precision, recall, and intersection over union (IOU), particularly excelling in class-wise IOUs for critical structures such as bladder, rectum, sigmoid colon, and femoral heads. Discussion: The architecture improved both boundary delineation and overall prediction quality. It is established that the proposed RU-Net shows its supremacy under dice loss conditions in terms of proper prediction and segmentation. Conclusion: RU-Net offers a robust solution for multiorgan segmentation in gynecological BT. Its advanced design effectively addresses anatomical distortions, demonstrating strong potential to improve clinical outcomes in magnetic resonance-guided treatment planning.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "name": "Suresh Das"
    },
    {
      "name": "Prasun Sanki"
    },
    {
      "name": "Siladittya Manna"
    },
    {
      "name": "Subhayan Mondal"
    },
    {
      "name": "Saumik Bhattacharya"
    },
    {
      "name": "Sayantari Ghosh"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5zery95qptav1.bib",
  "cited_by": 0,
  "doi": "10.4103/jmp.jmp_35_26",
  "fulltext_url": "https://www.ovid.com/jnls/jomp/fulltext/10.4103/jmp.jmp_35_26~multiorgan-segmentation-in-magnetic-resonance-images-for",
  "id": "5zery95qptav1",
  "issns": [
    "0971-6203",
    "1998-3913"
  ],
  "landing_url": "https://doi.org/10.4103/jmp.jmp_35_26",
  "language": "en",
  "licence": "CC-BY-NC-SA",
  "licence_label": "CC BY-NC-SA",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by-nc-sa/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "R895-920"
    ],
    [
      "openalex",
      "1707|Medical Image Segmentation Techniques"
    ]
  ],
  "openalex": "W7214101616",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-01",
  "publisher": "Wolters Kluwer Medknow Publications",
  "ris": "https://research.draconitic.com/paper/5zery95qptav1.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:43:57Z",
      "native_id": "846225970963483b926e057287629e3c",
      "source": "doaj",
      "url": "https://doi.org/10.4103/jmp.jmp_35_26"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:R895-920",
      "subfield": "general"
    }
  ],
  "title": "Multiorgan Segmentation in Magnetic Resonance Images for Gynecological Brachytherapy Using Residuals in Residual Dense Block",
  "title_text": "Multiorgan Segmentation in Magnetic Resonance Images for Gynecological Brachytherapy Using Residuals in Residual Dense Block",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:43:57Z",
  "url": "https://research.draconitic.com/paper/5zery95qptav1",
  "venue": "Journal of Medical Physics",
  "versions": []
}