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Efficient, reliable and reproducible target volume delineation is a key stepin the effective planning of breast radiotherapy. However, post-operativebreast target delineation is challenging as the contrast between the tumor bedvolume (TBV) and normal breast tissue is relatively low in CT images. In thisstudy, we propose to mimic the marker-guidance procedure in manual targetdelineation. 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 thelocation-related features, which underscores regions with high saliency levelsand suppresses low saliency regions. The saliency maps were generated byidentifying markers on CT images. Markers' locations were then converted toprobability maps using a distance-transformation coupled with a Gaussianfilter. Subsequently, the CT images and the corresponding saliency maps formeda multi-channel input for the SDL-Seg network. Our in-house dataset wascomprised 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. Ourmodel achieved mean (standard deviation) of 76.4 %, 6.76 mm, and 1.9 mm forDSC, HD95, and ASD respectively on the test set with computation time of below11 seconds per one CT volume. SDL-Seg showed superior performance relative tobasic U-Net for all the evaluation metrics while preserving low computationcost. The findings demonstrate that SDL-Seg is a promising approach forimproving the efficiency and accuracy of the on-line treatment planningprocedure of PBI, such as GammaPod based PBI.

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