  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.
