Cranial Implant Prediction using Low-Resolution 3D Shape Completion and High-Resolution 2D Refinement

TL;DR

This paper introduces a two-stage convolutional network that efficiently predicts high-resolution 3D cranial implants from low-resolution 3D shapes and 2D slice refinement, addressing GPU limitations and improving accuracy.

Contribution

It presents a novel end-to-end hierarchical network combining 3D shape completion and 2D slice refinement for cranial implant prediction.

Findings

Accurately predicts high-resolution 3D implants

Achieves high dice-score and Hausdorff distance metrics

Addresses GPU memory constraints effectively

Abstract

Designing of a cranial implant needs a 3D understanding of the complete skull shape. Thus, taking a 2D approach is sub-optimal, since a 2D model lacks a holistic 3D view of both the defective and healthy skulls. Further, loading the whole 3D skull shapes at its original image resolution is not feasible in commonly available GPUs. To mitigate these issues, we propose a fully convolutional network composed of two subnetworks. The first subnetwork is designed to complete the shape of the downsampled defective skull. The second subnetwork upsamples the reconstructed shape slice-wise. We train the 3D and 2D networks together end-to-end, with a hierarchical loss function. Our proposed solution accurately predicts a high-resolution 3D implant in the challenge test case in terms of dice-score and the Hausdorff distance.