We present a 3D deep learning framework that can generate a complete cranial model using a defective one. The Boolean subtraction between these two models generates the geometry of the implant required for surgical reconstruction. There is little or no need for post-processing to eliminate noise in the implant model generated by the proposed approach. The framework can be used to meet the repair needs of cranial imperfections caused by trauma, congenital defects, plastic surgery, or tumor resection. Traditional implant design methods for skull reconstruction rely on the mirror operation. However, these approaches have great limitations when the defect crosses the plane of symmetry or the patient's skull is asymmetrical. The proposed deep learning framework is based on an enhanced three-dimensional autoencoder. Each training sample for the framework is a pair consisting of a cranial model converted from CT images and a corresponding model with simulated defects on it. Our approach can learn the spatial distribution of the upper part of normal cranial bones and use flawed cranial data to predict its complete geometry. Empirical research on simulated defects and actual clinical applications shows that our framework can meet most of the requirements of cranioplasty.

This paper presents an effective cost aggregation strategy for dense stereo matching. Based on the guided image filtering (GIF), we propose a new aggregation scheme called Pervasive Guided Image Filtering (PGIF) to introduce weightings to the energy function of the filter which allows the whole image pair to be taken into account. The filter parameters of PGIF are calculated as two-dimensional convolution using the bright and spatial differences between the corresponding pixels, which can be incrementally calculated for efficient aggregation. The complexity of the proposed algorithm is O(N), which is linear to the number of image pixels. Furthermore, the algorithm can be further simplified into O(N/4) without significantly sacrificing accuracy if subsampling is applied in the stage of parameter calculation. We also found that a step function to attenuate noise is required in calculating the weights. Experimental evaluation on version 3 of the Middlebury stereo evaluation datasets shows that the proposed method achieves superior disparity accuracy over state-of-the-art aggregation methods with comparable processing speed.

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