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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Efficient application of deep learning-based elective lymph node regions delineation for pelvic malignancies.

Feng Wen^1,2, Jie Zhou³, Zhebin Chen⁴

¹Division of Abdominal Tumor Multimodality Treatment, Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.

Medical Physics

|July 29, 2024

Summary

A new deep learning model, CMU-net, automates pelvic lymph node region delineation for cancer radiotherapy, improving efficiency and consistency. This AI tool shows high accuracy, potentially streamlining clinical workflows for pelvic malignancies.

Keywords:

deep learning delineation pelvic lymph node regions pelvic malignancy radiotherapy

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Area of Science:

Radiotherapy
Medical Imaging
Artificial Intelligence

Background:

International consensus exists for pelvic lymph node region (LNR) delineation.
Significant inter- and intra-observer variability in LNR contouring persists.
Manual contouring for pelvic malignancies is time-consuming and labor-intensive.

Purpose of the Study:

To develop a deep learning model for automated pelvic LNR delineation.
To improve the accuracy and efficiency of contouring for pelvic cancer radiotherapy.

Main Methods:

Retrospective collection of planning CT scans from 160 patients with pelvic malignancies.
Delineation of six pelvic LNRs by two radiation oncologists as ground truth.
Construction and validation of a cascaded multi-heads U-net (CMU-net) model.

Main Results:

CMU-net achieved high accuracy in automatic delineation of six pelvic LNRs.
Dice Similarity Coefficient (DSC) ranged from 0.851 to 0.942.
Average Surface Distance (ASD) ranged from 0.381 to 1.037 mm, and 95th Percentile Hausdorff Distance (HD95) ranged from 2.025 to 3.697 mm.
Over 95% of auto-delineations required only minor edits by expert oncologists.

Conclusions:

The CMU-net model was successfully developed for automated pelvic LNR delineation.
The model demonstrated improved contouring efficiency and high consistency.
CMU-net shows potential for implementation in radiotherapy workflows for pelvic malignancies.