# Fully Automated Organ Segmentation in Male Pelvic CT Images

arXiv: https://arxiv.org/abs/1805.12526 · HTML: https://mlsanity.com/paper/1805.12526/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2018.

## Built on by (most cited first, 6 of 6)

- [PSA-Net: Deep Learning based Physician Style-Aware Segmentation Network for Post-Operative Prostate Cancer Clinical Target Volume](https://mlsanity.com/paper/2102.07880/) (2021)
- [Easy-GT: Open-Source Software to Facilitate Making the Ground Truth for White Blood Cells Nucleus](https://mlsanity.com/paper/2101.11654/) (2021)
- [Dosimetric impact of physician style variations in contouring CTV for post-operative prostate cancer: A deep learning-based simulation study](https://mlsanity.com/paper/2102.01006/) (2021)
- [Prior Guided Deep Difference Meta-Learner for Fast Adaptation to Stylized Segmentation](https://mlsanity.com/paper/2211.10588/) (2022)
- [Deep Learning (DL)-based Automatic Segmentation of the Internal Pudendal Artery (IPA) for Reduction of Erectile Dysfunction in Definitive Radiotherapy of Localized Prostate Cancer](https://mlsanity.com/paper/2302.01493/) (2023)
- [Improving Prostate Gland Segmentation Using Transformer based Architectures](https://mlsanity.com/paper/2506.14844/) (2025)

## Abstract (arXiv)

Accurate segmentation of prostate and surrounding organs at risk is important for prostate cancer radiotherapy treatment planning. We present a fully automated workflow for male pelvic CT image segmentation using deep learning. The architecture consists of a 2D localization network followed by a 3D segmentation network for volumetric segmentation of prostate, bladder, rectum, and femoral heads. We used a multi-channel 2D U-Net followed by a 3D U-Net with encoding arm modified with aggregated residual networks, known as ResNeXt. The models were trained and tested on a pelvic CT image dataset comprising 136 patients. Test results show that 3D U-Net based segmentation achieves mean (SD) Dice coefficient values of 90 (2.0)% ,96 (3.0)%, 95 (1.3)%, 95 (1.5)%, and 84 (3.7)% for prostate, left femoral head, right femoral head, bladder, and rectum, respectively, using the proposed fully automated segmentation method.