Deep Learning using Pre-Brachytherapy MRI to Automatically Predict Applicator Induced Complex Uterine Deformation

This novel deep-learning (DL) algorithm addresses the challenging task of predicting uterine shape and location when deformed from its natural anatomy by the presence of an intrauterine (tandem)/intravaginal (ring) applicator during brachytherapy (BT) treatment for locally advanced cervical cancer. Paired pelvic MRI datasets from 92 subjects, acquired without (pre-BT) and with (at-BT) applicators, were used. We propose a novel automated algorithm to segment the uterus in pre-BT MR images using a deep convolutional neural network (CNN) incorporated with autoencoders. The proposed neural net is based on a pre-trained CNN Inception V4 architecture. It predicts a compressed vector by applying a multi-layer autoencoder, which is then back-projected into the segmentation contour of the uterus. Following this, another transfer learning approach using a modified U-net model is employed to predict the at-BT uterus shape from pre-BT MRI. The complex and large deformations of the uterus are quantified using free form deformation method. The proposed algorithm yielded an average Dice Coefficient (DC) of
$94.1\pm 3.3$
and an average Hausdorff Distance (HD) of
$4.0\pm 3.1$
mm compared to the manually defined ground truth by expert clinicians. Further, the modified U-net prediction of the at-BT uterus resulted in a DC accuracy of
$88.1\pm 3.8$
and HD of
$5.8\pm 3.6$
mm. The mean uterine surface point-to-point displacement was 25.0 [10.0–62.5] mm from the pre-BT position. Our unique DL method can thus successfully predict tandem-deformed uterine shape and position from MR images taken before the BT implant procedure i.e. without the applicator in place. Clinical relevance—The proposed DL-based framework can be incorporated as an automatic prediction tool of uterine deformation due to applicator insertion for personalized BT treatments. It holds promise for more streamlined clinical/technical decision-making before BT applicator insertion resulting in improved dosimetric outcomes.