Accuracy and efficiency of automatic tooth segmentation in digital dental models using deep learning

This study evaluates the accuracy and efficiency of automatic tooth segmentation in digital dental models using deep learning. We developed a dynamic graph convolutional neural network (DGCNN)-based algorithm for automatic tooth segmentation and classification using 516 digital dental models. We segmented 30 digital dental models using three methods for comparison: (1) automatic tooth segmentation (AS) using the DGCNN-based algorithm from LaonSetup software, (2) landmark-based tooth segmentation (LS) using OrthoAnalyzer software, and (3) tooth designation and segmentation (DS) using Autolign software. We evaluated the segmentation success rate, mesiodistal (MD) width, clinical crown height (CCH), and segmentation time. For the AS, LS, and DS, the tooth segmentation success rates were 97.26%, 97.14%, and 87.86%, respectively (p DS), the means of MD widths were 8.51, 8.28, and 8.63 mm, respectively (p AS > LS), the means of CCHs were 7.58, 7.65, and 7.52 mm, respectively (p DS, AS), and the means of segmentation times were 57.73, 424.17, and 150.73 s, respectively (p < 0.001, post-hoc; AS < DS < LS). Automatic tooth segmentation of a digital dental model using deep learning showed high segmentation success rate, accuracy, and efficiency; thus, it can be used for orthodontic diagnosis and appliance fabrication.ope

Deep learning approach to semantic segmentation in 3D point cloud intra-oral scans of teeth

Accurate segmentation of data, derived from intra-oral scans (IOS), is a crucial step in a computer-aided design (CAD) system for many clinical tasks, such as implantology and orthodontics in modern dentistry. In order to reach the highest possible quality, a segmentation model may process a point cloud derived from an IOS in its highest available spatial resolution, especially for performing a valid analysis in finely detailed regions such as the curvatures in border lines between two teeth. In this paper, we propose an end-to-end deep learning framework for semantic segmentation of individual teeth as well as the gingiva from point clouds representing IOS. By introducing a non-uniform resampling technique, our proposed model is trained and deployed on the highest available spatial resolution where it learns the local fine details along with the global coarse structure of IOS. Furthermore, the point-wise cross-entropy loss for semantic segmentation of a point cloud is an ill-posed problem, since the relative geometrical structures between the instances (e.g. the teeth) are not formulated. By training a secondary simple network as a discriminator in an adversarial setting and penalizing unrealistic arrangements of assigned labels to the teeth on the dental arch, we improve the segmentation results considerably. Hence, a heavy post-processing stage for relational and dependency modeling (e.g. iterative energy minimization of a constructed graph) is not required anymore. Our experiments show that the proposed approach improves the performance of our baseline network and outperforms the state-of-the-art networks by achieving 0.94 IOU score

Applications of Markov Random Field Optimization and 3D Neural Network Pruning in Computer Vision

Recent years witness the rapid development of Convolutional Neural Network (CNN) in various computer vision applications that were traditionally addressed by Markov Random Field (MRF) optimization methods. Even though CNN based methods achieve high accuracy in these tasks, a high level of fine results are difficult to be achieved. For instance, a pairwise MRF optimization method is capable of segmenting objects with the auxiliary edge information through the second-order terms, which is very uncertain to be achieved by a deep neural network. MRF optimization methods, however, are able to enhance the performance with an explicit theoretical and experimental supports using iterative energy minimization. Secondly, such an edge detector can be learned by CNNs, and thus, seeking to transfer the task of a CNN for another task becomes valuable. It is desirable to fuse the superpixel contours from a state-of-the-art CNN with semantic segmentation results from another state-of-the-art CNN so that such a fusion enhances the object contours in semantic segmentation to be aligned with the superpixel contours. This kind of fusion is not limited to semantic segmentation but also other tasks with a collective effect of multiple off-the-shelf CNNs. While fusing multiple CNNs is useful to enhance the performance, each of such CNNs is usually specifically designed and trained with an empirical configuration of resources. With such a large batch size, however, the joint CNN training is possible to be out of GPU memory. Such a problem is usually involved in efficient CNN training yet with limited resources. This issue is more obvious and severe in 3D CNNs than 2D CNNs due to the high requirement of training resources. To solve the first problem, we propose two fast and differentiable message passing algorithms, namely Iterative Semi-Global Matching Revised (ISGMR) and Parallel Tree-Reweighted Message Passing (TRWP), for both energy minimization problems and deep learning applications. Our experiments on stereo vision dataset and image inpainting dataset validate the effectiveness and efficiency of our methods with minimum energies comparable to the state-of-the-art algorithm TRWS and greatly improve the forward and backward propagation speed using CUDA programming on massive parallel trees. Applying these two methods on deep learning semantic segmentation on PASCAL VOC 2012 with Canny edges achieves enhanced segmentation results measured by mean Intersection over Union (mIoU). In the second problem, to effectively fuse and finetune multiple CNNs, we present a transparent initialization module that identically maps the output of a multiple-layer module to its input at the early stage of finetuning. The pretrained model parameters are then gradually divergent in training as the loss decreases. This transparent initialization has a higher initialization rate than Net2Net and a higher recovery rate compared with random initialization and Xavier initialization. Our experiments validate the effectiveness of the proposed transparent initialization and the sparse encoder with sparse matrix operations. The edges of segmented objects achieve a higher performance ratio and a higher F-measure than other comparable methods. In the third problem, to compress a CNN effectually, especially for resource-inefficient 3D CNNs, we propose a single-shot neuron pruning method with resource constraints. The pruning principle is to remove the neurons with low neuron importance corresponding to small connection sensitivities. The reweighting strategy with the layerwise consumption of memory or FLOPs improves the pruning ability by avoiding infeasible pruning of the whole layer(s). Our experiments on point cloud dataset, ShapeNet, and medical image dataset, BraTS'18, prove the effectiveness of our method. Applying our method to video classification on UCF101 dataset using MobileNetV2 and I3D further strengthens the benefits of our method