Automatic implant shape design for minimally invasive repair of pectus excavatum using deep learning and shape registration.

Minimally invasive repair of pectus excavatum (MIRPE) is an effective method for correcting pectus excavatum (PE), a congenital chest wall deformity characterized by concave depression of the sternum. In MIRPE, a long, thin, curved stainless plate (implant) is placed across the thoracic cage to correct the deformity. However, the implant curvature is difficult to accurately determine during the procedure. This implant depends on the surgeon’s expert knowledge and experience and lacks objective criteria. Moreover, tedious manual input by surgeons is required to estimate the implant shape. In this study, a novel three-step end-to-end automatic framework is proposed to determine the implant shape during preoperative planning: (1) The deepest depression point (DDP) in the sagittal plane of the patient’s CT volume is automatically determined using Sparse R-CNN-R101, and the axial slice containing the point is extracted. (2) Cascade Mask R-CNN-X101 segments the anterior intercostal gristle of the pectus, sternum and rib in the axial slice, and the contour is extracted to generate the PE point set. (3) Robust shape registration is performed to match the PE shape with a healthy thoracic cage, which is then utilized to generate the implant shape. The framework was evaluated on a CT dataset of 90 PE patients and 30 healthy children. The experimental results show that the average error of the DDP extraction was 5.83 mm. The end-to-end output of our framework was compared with surgical outcomes of professional surgeons to clinically validate the effectiveness of our method. The results indicate that the root mean square error (RMSE) between the midline of the real implant and our framework output was less than 2 mm.