Tooth instance segmentation from cone-beam CT images through point-based detection and Gaussian disentanglement

Multimedia Tools and Applications - Individual tooth segmentation and identification from cone-beam computed tomography images are preoperative prerequisites for orthodontic treatments. Instance...     

Fast high-quality volume ray-casting with virtual samplings

Volume ray-casting with a higher order reconstruction filter and/or a higher sampling rate has been adopted in direct volume rendering frameworks to provide a smooth reconstruction of the volume scalar and/or to reduce artifacts when the combined frequency of the volume and transfer function is high. While it enables high-quality volume rendering, it cannot support interactive rendering due to its high computational cost. In this paper, we propose a fast high-quality volume ray-casting algorithm which effectively increases the sampling rate. While a ray traverses the volume, intensity values are uniformly reconstructed using a high-order convolution filter. Additional samplings, referred to as virtual samplings, are carried out within a ray segment from a cubic spline curve interpolating those uniformly reconstructed intensities. These virtual samplings are performed by evaluating the polynomial function of the cubic spline curve via simple arithmetic operations. The min max blocks are refined accordingly for accurate empty space skipping in the proposed method. Experimental results demonstrate that the proposed algorithm, also exploiting fast cubic texture filtering supported by programmable GPUs, offers renderings as good as a conventional ray-casting algorithm using high-order reconstruction filtering at the same sampling rate, while delivering 2.5x to 3.3x rendering speed-up.     

Automatic extraction of inferior alveolar nerve canal using feature-enhancing panoramic volume rendering

Dental implant surgery, which involves the surgical insertion of a dental implant into the jawbone as an artificial root, has become one of the most successful applications of computed tomography (CT) in dental implantology. For successful implant surgery, it is essential to identify vital anatomic structures such as the inferior alveolar nerve (IAN), which should be avoided during the surgical procedure. Due to the ambiguity of its structure, the IAN is very elusive to extract in dental CT images. As a result, the IAN canal is typically identified in most previous studies. This paper presents a novel method of automatically extracting the IAN canal. Mental and mandibular foramens, which are regarded as the ends of the IAN canal in the mandible, are detected automatically using 3-D panoramic volume rendering (VR) and texture analysis techniques. In the 3-D panoramic VR, novel color shading and compositing methods are proposed to emphasize the foramens and isolate them from other fine structures. Subsequently, the path of the IAN canal is computed using a line-tracking algorithm. Finally, the IAN canal is extracted by expanding the region of the path using a fast marching method with a new speed function exploiting the anatomical information about the canal radius. In experimental results using ten clinical datasets, the proposed method identified the IAN canal accurately, demonstrating that this approach assists dentists substantially during dental implant surgery.     

GGO nodule volume-preserving nonrigid lung registration using GLCM texture analysis

In lung cancer screening, benign and malignant nodules can be classified through nodule growth assessment by the registration and, then, subtraction between follow-up computed tomography scans. During the registration, the volume of nodule regions in the floating image should be preserved, whereas the volume of other regions in the floating image should be aligned to that in the reference image. However, ground glass opacity (GGO) nodules are very elusive to automatically segment due to their inhomogeneous interior. In other words, it is difficult to automatically define the volume-preserving regions of GGO nodules. In this paper, we propose an accurate and fast nonrigid registration method. It applies the volume-preserving constraint to candidate regions of GGO nodules, which are automatically detected by gray-level cooccurrence matrix (GLCM) texture analysis. Considering that GGO nodules can be characterized by their inner inhomogeneity and high intensity, we identify the candidate regions of GGO nodules based on the homogeneity values calculated by the GLCM and the intensity values. Furthermore, we accelerate our nonrigid registration by using Compute Unified Device Architecture (CUDA). In the nonrigid registration process, the computationally expensive procedures of the floating-image transformation and the cost-function calculation are accelerated by using CUDA. The experimental results demonstrated that our method almost perfectly preserves the volume of GGO nodules in the floating image as well as effectively aligns the lung between the reference and floating images. Regarding the computational performance, our CUDA-based method delivers about 20× faster registration than the conventional method. Our method can be successfully applied to a GGO nodule follow-up study and can be extended to the volume-preserving registration and subtraction of specific diseases in other organs (e.g., liver cancer).     

Automatic teeth axes calculation for well-aligned teeth using cost profile analysis along teeth center arch

In dental implantology and virtual dental surgery planning using computed tomography (CT) images, the examination of the axes of neighboring and/or biting teeth is important to improve the performance of the masticatory system as well as the aesthetic beauty. However, due to its high connectivity to neighboring teeth and jawbones, a tooth and/or its axis is very elusive to automatically identify in dental CT images. This paper presents a novel method of automatically calculating individual teeth axes. The planes separating the individual teeth are automatically calculated using cost profile analysis along the teeth center arch. In this calculation, a novel plane cost function, which considers the intensity and the gradient, is proposed to favor the teeth separation planes crossing the teeth interstice and suppress the possible inappropriately detected separation planes crossing the soft pulp. The soft pulp and dentine of each individually separated tooth are then segmented by a fast marching method with two newly proposed speed functions considering their own specific anatomical characteristics. The axis of each tooth is finally calculated using principal component analysis on the segmented soft pulp and dentine. In experimental results using 20 clinical datasets, the average angle and minimum distance differences between the teeth axes manually specified by two dentists and automatically calculated by the proposed method were 1.94° ± 0.61° and 1.13 ± 0.56 mm, respectively. The proposed method identified the individual teeth axes accurately, demonstrating that it can give dentists substantial assistance during dental surgery such as dental implant placement and orthognathic surgery.