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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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Published on: July 5, 2024

Deep CNN-based Fully Automated Segmentation of Pelvic Multi-Organ on CT Images for Prostate Cancer Radiotherapy.

Bahram Mofid¹, Sayed Mohammad Modarres Mosalla², Masumeh Goodarzi³

¹Department of Radiation Oncology, Shohadae-Tajrish Medical Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Journal of Biomedical Physics & Engineering

|December 11, 2025

Summary

Deep learning auto-segmentation using 3D nnU-net shows promise for prostate radiotherapy, accurately delineating organs at risk. This method offers a faster and more consistent alternative to manual contouring for treatment planning.

Keywords:

3D nnU-net Automatic Segmentation Deep Learning Machine Learning Neural Networks Prostate Cancer Radiotherapy

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Area of Science:

Medical Physics
Radiotherapy
Artificial Intelligence

Background:

Manual delineation of organs for prostate radiotherapy is time-consuming and variable.
Deep learning auto-segmentation offers accurate and high-fidelity contours.

Purpose of the Study:

To evaluate a Computed Tomography (CT)-based deep learning auto-segmentation algorithm for multi-organ delineation in prostate radiotherapy.

Main Methods:

A retrospective study included 118 prostate cancer patients.
3D nnU-net deep convolutional neural network was used for auto-contouring.
Manual and automatic contours were compared using Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD), with Dose-Volume Histograms (DVH) for plan evaluation.

Main Results:

3D nnU-net achieved high performance for bladder, femur heads, and rectum.
Prostate, lymph nodes, and seminal vesicles segmentation showed varying degrees of accuracy.
Significant differences in DVH parameters were observed between manual and automatic contours for most organs at risk.

Conclusions:

The 3D nnU-net architecture is effective for multi-organ segmentation in the male pelvic area for radiotherapy.