Inoperable endometrial cancer may be treated with curative aim using radical radiotherapy alone. The radiation techniques are external beam radiotherapy (EBRT) alone, EBRT plus brachytherapy and brachytherapy alone. Recently, high-dose-rate brachytherapy has been used instead of low-dose-rate brachytherapy. Image-guided brachytherapy enables sufficient coverage of tumour and reduction of dose to the organs at risk, thus increasing the therapeutic ratio of treatment. Local control rates with three-dimensional brachytherapy appear better than with conventional techniques (about 90-100% and 70-90%, respectively).

This research proposes augmenting cropped computed tomography (CT) slices with data attributes to enhance the performance of a deep-learning-based automatic left-femur segmentation scheme. The data attribute is the lying position for the left-femur model. In the study, the deep-learning-based automatic left-femur segmentation scheme was trained, validated, and tested using eight categories of CT input datasets for the left femur (F-I–F-VIII). The segmentation performance was assessed by Dice similarity coefficient (DSC) and intersection over union (IoU); and the similarity between the predicted 3D reconstruction images and ground-truth images was determined by spectral angle mapper (SAM) and structural similarity index measure (SSIM). The left-femur segmentation model achieved the highest DSC (88.25%) and IoU (80.85%) under category F-IV (using cropped and augmented CT input datasets with large feature coefficients), with an SAM and SSIM of 0.117–0.215 and 0.701–0.732. The novelty of this research lies in the use of attribute augmentation in medical image preprocessing to enhance the performance of the deep-learning-based automatic left-femur segmentation scheme.