Deep Learning-based Anatomy-Aware Morph Model for Registration of Prostate Whole-Mount Histopathology to MRI.

Purpose To develop and evaluate a novel deep learning-based approach for registering presurgical MR and whole-mount histopathology (WMHP) images of the prostate. Materials and Methods This retrospective study included patients who underwent prostate MRI before radical prostatectomy between July 2016 and June 2020. High-resolution ex vivo MRI was used as a reference to assess the structural relationship between in vivo MRI and WMHP. An Anatomy-Aware Morph model, a hybrid attention and convolutional neural network-based approach, was developed for multimodality prostate image registration. The pipeline included a module to estimate and correct distortion and motion between the prostate specimen and outside the human body. The dataset was divided into 270 and 45 patients for training and testing, respectively. Registration accuracy was evaluated using Dice similarity coefficient (DSC), Hausdorff distance, and target registration error. Results The proposed approach was validated using 160 images extracted from 45 male patients in the testing dataset with the average age ± SD of 64.0 years ± 6.6. The method achieved a DSC and Hausdorff distance of 0.95 ± 0.06 and 1.84 mm ± 0.38. The two-dimensional target registration errors between 90 sets of landmarks on in vivo MR images and WMHP images were 3.93 mm ± 0.80 and 1.18 mm ± 0.28 before and after registration ( < .001). The developed algorithm significantly outperformed the state-of-the-art VoxelMorph method for multimodality prostate image registration ( < .0001 for both DSC and Hausdorff distance). Conclusion The developed registration method successfully aligned presurgical prostate MR and histopathology images, facilitating automated mapping of prostate cancer from WMHP to MRI. Affine Transformation, Deformable Registration, Prostate Magnetic Resonance Imaging, Prostate Whole-Mount Histopathology © RSNA, 2025.