Background:Computer-assisted mandibular reconstruction facilitates preoperative surgery simulation and transfers the virtual plan to a real operation. This systematic review and meta-analysis aimed to compare the accuracy, efficiency, postoperative complications, and economic viability between compu

Computer-Assisted versus Conventional Freehand Mandibular Reconstruction with Fibula Free Flap: A Systematic Review and Meta-Analysis.

The human cerebrovasculature is extremely complicated and its three dimensional (3D) highly parcellated models, though necessary, are unavailable. We constructed a digital cerebrovascular model from a high resolution, 3T 3D time-of-flight magnetic resonance angiography scan. This model contains the arterial and venous systems and is 3D, geometric, highly parcellated, fully segmented, and completely labeled with name, diameter, and variants. Our approach replaces the tedious and time consuming process of checking and correcting automatic segmentation results done at 2D image level with an aggregate and faster process at 3D model level. The creation of the vascular model required vessel pre-segmentation, centerline extraction, vascular segments connection, centerline smoothing, vessel surface construction, vessel grouping, tracking, editing, labeling, setting diameter, and checking correctness and completeness. For comparison, the same scan was segmented automatically with 59.8% sensitivity and only 16.5% of vessels smaller than 1 pixel size were extracted. To check and correct this automatic segmentation requires 8 weeks. Conversely, the speedup of our approach (the number of 2D segmented areas/the number of 3D vascular segments) is 34. This cerebrovascular model can serve as a reference framework in clinical, research, and educational applications. The wealth of information aggregated with its quantification capabilities can augment or replace numerous textbook chapters. Five applications of the vascular model were described. The model is easily extendable in content, parcellation, and labeling, and the proposed approach is applicable for building a whole body vascular system.

A 3D Model of Human Cerebrovasculature Derived from 3T Magnetic Resonance Angiography

Due to its importance and possible applications in visualization, tumor detection and preoperative planning, automatic small bowel segmentation is essential for computer-aided diagnosis of small bowel pathology. However, segmenting the small bowel directly on computed tomography (CT) scans is very difficult because of the low image contrast on CT scans and high tortuosity of the small bowel and its close proximity to other abdominal organs. Motivated by the intensity characteristics of abdominal CT images, the anatomic relationship between the mesenteric vasculature and the small bowel, and potential usefulness of the mesenteric vasculature for establishing the path of the small bowel, we propose a novel mesenteric vasculature map-guided method for small bowel segmentation on high-resolution CT angiography scans. The major mesenteric arteries are first segmented using a vessel tracing method based on multi-linear subspace vessel model and Bayesian inference. Second, multi-view, multi-scale vesselness enhancement filters are used to segment small vessels, and vessels directly or indirectly connecting to the superior mesenteric artery are classified as mesenteric vessels. Third, a mesenteric vasculature map is built by linking vessel bifurcation points, and the small bowel is segmented by employing the mesenteric vessel map and fuzzy connectness. The method was evaluated on 11 abdominal CT scans of patients suspected of having carcinoid tumors with manually labeled reference standard. The result, 82.5% volume overlap accuracy compared with the reference standard, shows it is feasible to segment the small bowel on CT scans using the mesenteric vasculature as a roadmap.

Mesenteric Vasculature-Guided Small Bowel Segmentation on 3-D CT

Classification of acute leukemia using medical-knowledge-based morphology and CD marker

Our motivation was to provide an automatic tool for radiologists and orthopedic surgeons for improving the quality of life of an aging population We propose a method for generating a shape model and a fully automated segmenting scheme for the psoas major muscle in X-ray CT images by using the shape model Our approach consists of two steps: (1) The generation of a shape model and its application to muscle segmentation The shape model describes the muscle’s outer shape and has two parameters, an outer shape parameter and a fitting parameter The former was determined by approximating of the outer shape of the muscle region in training cases The latter was determined for each test case in the recognition process (2) Finally, the psoas major muscle was segmented by use of the shape model To evaluate the performance of the method, we applied it to CT images for constructing the shape models by using 20 cases as training samples; 80 cases were used for testing The accuracy of this method was measured by comparison of the extracted muscle regions with regions that were identified manually by an expert radiologist The experimental results of the segmentation of the psoas major muscle gave a mean Jaccard similarity coefficient of 723% The mean true segmentation coefficient was 762% The proposed method can be used for the analysis of cross-sectional area and muscular thickness in a transverse section, offering radiologists an alternative to manual measurement for saving their time and improving the reproducibility of segmentation

http://www.fjt.info.gifu-u.ac.jp/publication/708.pdf

Automated segmentation of psoas major muscle in X-ray CT images by use of a shape model: preliminary study

The use of the watershed algorithm for image segmentation is widespread because it is able to produce a complete division of the image. However, it is susceptible to over-segmentation and in medical image segmentation, this meant that that we do not have good representations of the anatomy. We address this issue by thresholding the gradient magnitude image and performing post-segmentation merging on the initial segmentation map. The automated thresholding technique is based on the histogram of the gradient magnitude map while the post-segmentation merging is based on the similarity in textural features (namely angular second moment, contrast, entropy and inverse difference moment) belonging to two neighboring partitions. When applied to the segmentation of various facial anatomical structures from magnetic resonance (MR) images, the proposed method achieved an overlap index of 92.6% compared to manual contour tracings. It is able to merge more than 80% of the initial partitions, which indicates that a large amount of over-segmentation has been reduced. Results produced using watershed algorithm with and without the proposed and proposed post-segmentation merging are presented for comparisons.

Medical image segmentation using watershed segmentation with texture-based region merging

A method is proposed for 3D segmentation and quantification of the masseter muscle from magnetic resonance (MR) images, which is often performed in pre-surgical planning and diagnosis. Because of a lack of suitable automatic techniques, a common practice is for clinicians to manually trace out all relevant regions from the image slices which is extremely time-consuming. The proposed method allows significant time savings. In the proposed method, a patient-specific masseter model is built from a test dataset after determining the dominant slices that represent the salient features of the 3D muscle shape from training datasets. Segmentation is carried out only on these slices in the test dataset, with shape-based interpolation then applied to build the patient-specific model, which serves as a coarse segmentation of the masseter. This is first refined by matching the intensity distribution within the masseter volume against the distribution estimated from the segmentations in the dominant slices, and further refined through boundary analysis where the homogeneity of the intensities of the boundary pixels is analyzed and outliers removed. It was observed that the left and right masseter muscles’ volumes in young adults (28.54 and 27.72cm3) are higher than those of older (ethnic group removed) adults (23.16 and 22.13cm3). Evaluation indicates good agreement between the segmentations and manual tracings, with average overlap indexes for the left and right masseters at 86.6% and 87.5% respectively.

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3D Segmentation and Quantification of a Masticatory Muscle from MR Data Using Patient-Specific Models and Matching Distributions

Objective The muscles of mastication play a major role in the orodigestive system as the principal motive force for the mandible. An algorithm for segmenting these muscles from magnetic resonance (MR) images was developed and tested. Materials and methods Anatomical information about the muscles of mastication in MR images is used to obtain the spatial relationships relating the muscle region of interest (ROI) and head ROI. A model-based technique that involves the spatial relationships between head and muscle ROIs as well as muscle templates is developed. In the segmentation stage, the muscle ROI is derived from the model. Within the muscle ROI, anisotropic diffusion is applied to smooth the texture, followed by thresholding to exclude bone and fat. The muscle template and morphological operators are employed to obtain an initial estimate of the muscle boundary, which then serves as the input contour to the gradient vector flow snake that iterates to the final segmentation. Results The method was applied to segmentation of the masseter, lateral pterygoid and medial pterygoid in 75 images. The overlap indices (κ) achieved are 91.4, 92.1 and 91.2%, respectively. Conclusion A model-based method for segmenting the muscles of mastication from MR images was developed and tested. The results show good agreement between manual and automatic segmentations.

Muscles of mastication model-based MR image segmentation

An improved fuzzy C-means (FCM) clustering method is proposed It incorporates Otsu thresholding with conventional FCM to reduce FCM's susceptibility to local minima, as well as its tendency to derive a threshold that is biased towards the component with larger probability, and derive threshold values with greater accuracy Thresholding is performed at the cluster boundary region in feature space A comparison of the results produced by improved and conventional algorithms confirms the superior performance of the former

Fuzzy c-means algorithm with local thresholding for gray-scale images

Objective
In planning of maxillofacial surgeries, analysis and quantification of facial anatomical structures are carried out. At CARS 2007, we proposed a method to determine shape determinative slices, which captures the salient features of the shape of the 3D anatomical structure, to facilitate building of patient-specific models and rapid quantification. The accuracy of the built models was satisfactory. Here we propose an improved method that improves the accuracy of the built models through automatic refinement on the choice of the shape determinative slices by incorporating information from test dataset.

Hsiao Piau Ng

Papers

Medical image segmentation using watershed segmentation with texture-based region merging

3D Segmentation and Quantification of a Masticatory Muscle from MR Data Using Patient-Specific Models and Matching Distributions

Muscles of mastication model-based MR image segmentation

Fuzzy c-means algorithm with local thresholding for gray-scale images

An improved shape determinative slice determination method for patient-specific modeling of facial anatomical structure