Saliency-Guided Deep Learning Network for Automatic Tumor Bed Volume Delineation in Post-operative Breast Irradiation

TL;DR

This paper introduces a saliency-guided deep learning segmentation method that improves the accuracy and speed of tumor bed volume delineation in post-operative breast irradiation CT images by incorporating marker location cues.

Contribution

The study presents a novel SDL-Seg algorithm that integrates saliency information into a U-Net model for precise TBV segmentation, outperforming basic U-Net in accuracy and efficiency.

Findings

SDL-Seg achieved 76.4% DSC, 6.76 mm HD95, 1.9 mm ASD.

The method operates in under 11 seconds per CT volume.

SDL-Seg outperforms basic U-Net in all evaluation metrics.

Abstract

Efficient, reliable and reproducible target volume delineation is a key step in the effective planning of breast radiotherapy. However, post-operative breast target delineation is challenging as the contrast between the tumor bed volume (TBV) and normal breast tissue is relatively low in CT images. In this study, we propose to mimic the marker-guidance procedure in manual target delineation. We developed a saliency-based deep learning segmentation (SDL-Seg) algorithm for accurate TBV segmentation in post-operative breast irradiation. The SDL-Seg algorithm incorporates saliency information in the form of markers' location cues into a U-Net model. The design forces the model to encode the location-related features, which underscores regions with high saliency levels and suppresses low saliency regions. The saliency maps were generated by identifying markers on CT images. Markers' locations were then converted to probability maps using a distance-transformation coupled with a Gaussian filter. Subsequently, the CT images and the corresponding saliency maps formed a multi-channel input for the SDL-Seg network. Our in-house dataset was comprised of 145 prone CT images from 29 post-operative breast cancer patients, who received 5-fraction partial breast irradiation (PBI) regimen on GammaPod. The performance of the proposed method was compared against basic U-Net. Our model achieved mean (standard deviation) of 76.4 %, 6.76 mm, and 1.9 mm for DSC, HD95, and ASD respectively on the test set with computation time of below 11 seconds per one CT volume. SDL-Seg showed superior performance relative to basic U-Net for all the evaluation metrics while preserving low computation cost. The findings demonstrate that SDL-Seg is a promising approach for improving the efficiency and accuracy of the on-line treatment planning procedure of PBI, such as GammaPod based PBI.