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Detections of the uterus using preoperative magnetic resonance imaging (MRI) data are required for intraoperative navigation in High-Intensity Focused Ultrasound (HIFU) treatment of uterine fibroids. The modelling of the intraoperative uterus between empty and full bladder anatomy deems to be a crucial step to quantify uterus deformation caused by the bladder-and-rectum-filling (BRF) technique. In this paper, a novel two-stage conditional generative adversarial network (cGAN) is proposed to perform intraoperative uterus deformable reconstruction with anatomical constraints using only single preoperative MRI data, viz. ACDeformRec. The highly constrained anatomy properties are further captured by a novel attention network that formulates high-level uterus segmentation task by incorporating correlations of surrounding organs for more anatomically plausible reconstruction. The experimental result demonstrates the robustness of ACDeformRec by evaluating its performance on 181 clinical datasets and has achieved the lowest reconstruction error of 0.735 ± 0.045 mm with Dice Similarity Coefficient of 94.23% and Normalised Cross Correlation of 97.23%.

Anatomically constrained deformable 3D reconstruction of intraoperative uterus from preoperative MRI data on uterine fibroid treatment