Multiorgan Segmentation in Magnetic Resonance Images for Gynecological Brachytherapy Using Residuals in Residual Dense Block

S Das; P Sanki; S Manna; S Mondal; S Bhattacharya; S Ghosh

doi:10.4103/jmp.jmp_35_26

Abstract

RU-Net offers a robust solution for multiorgan segmentation in gynecological BT.

More works

Breaking the Privacy Promise: Gradient Inversion Attacks on Federated Low-Rank Adapted Vision Transformers Winter Conference on Applications of Computer Vision (WACV 2027) 2027
Bridging the Applicator Gap with Data-Doping: Dual-Domain Learning for Precise Bladder Segmentation in CT-Guided Brachytherapy arXiv preprint 2026
On the Role of the Projector in Contrastive Self-Supervised Learning: Last-Layer Rank Dynamics Drive Representation Quality arXiv preprint 2026
Reliability-Aware Weighted Multi-Scale Spatio-Temporal Maps for Heart Rate Monitoring IEEE International Conference on Image Processing (ICIP 2026) 2026
Semi-Supervised Sperm Motility Classification Using WHO Kinematic Features and Domain-Adapted Detection on VISEM-Tracking The First Workshop on AI in Fertility Science (2026) 2026
Dynamically Scaled Temperature in Self-Supervised Contrastive Learning IEEE Transactions on Artificial Intelligence 2025

All publications →

BibTeX

@article{das2026multiorgan,
  title={Multiorgan Segmentation in Magnetic Resonance Images for Gynecological Brachytherapy Using Residuals in Residual Dense Block},
  author={Das, S and Sanki, P and Manna, S and Mondal, S and Bhattacharya, S and Ghosh, S},
  journal={Journal of Medical Physics},
  year={2026},
  doi={10.4103/jmp.jmp_35_26},
  url={https://sadimanna.github.io/papers/rrdb-multiorgan/}
}