Segmentation of the temporalis muscle from MR data

Objective A method for segmenting the temporalis from magnetic resonance (MR) images was developed and tested. The temporalis muscle is one of the muscles of mastication which plays a major role in the mastication system. Materials and methods The temporalis region of interest (ROI) and the head ROI are defined in reference images, from which the spatial relationship between the two ROIs is derived. This relationship is used to define the temporalis ROI in a study image. Range-constrained thresholding is then employed to remove the fat, bone marrow and muscle tendon in the ROI. Adaptive morphological operations are then applied to first remove the brain tissue, followed by the removal of the other soft tissues surrounding the temporalis. Ten adult head MR data sets were processed to test this method. Results Using five data sets each for training and testing, the method was applied to the segmentation of the temporalis in 25 MR images (five from each test set). An average overlap index (κ) of 90.2% was obtained. Applying a leave-one-out evaluation method, an average κ of 90.5% was obtained from 50 test images. Conclusion A method for segmenting the temporalis from MR images was developed and tested on in vivo data sets. The results show that there is consistency between manual and automatic segmentations.     

Segmentation of tongue muscles from super-resolution magnetic resonance images

Imaging and quantification of tongue anatomy is helpful in surgical planning, post-operative rehabilitation of tongue cancer patients, and studying of how humans adapt and learn new strategies for breathing, swallowing and speaking to compensate for changes in function caused by disease, medical interventions or aging. In vivo acquisition of high-resolution three-dimensional (3D) magnetic resonance (MR) images with clearly visible tongue muscles is currently not feasible because of breathing and involuntary swallowing motions that occur over lengthy imaging times. However, recent advances in image reconstruction now allow the generation of super-resolution 3D MR images from sets of orthogonal images, acquired at a high in-plane resolution and combined using super-resolution techniques. This paper presents, to the best of our knowledge, the first attempt towards automatic tongue muscle segmentation from MR images. We devised a database of ten super-resolution 3D MR images, in which the genioglossus and inferior longitudinalis tongue muscles were manually segmented and annotated with landmarks. We demonstrate the feasibility of segmenting the muscles of interest automatically by applying the landmark-based game-theoretic framework (GTF), where a landmark detector based on Haar-like features and an optimal assignment-based shape representation were integrated. The obtained segmentation results were validated against an independent manual segmentation performed by a second observer, as well as against B-splines and demons atlasing approaches. The segmentation performance resulted in mean Dice coefficients of 85.3%, 81.8%, 78.8% and 75.8% for the second observer, GTF, B-splines atlasing and demons atlasing, respectively. The obtained level of segmentation accuracy indicates that computerized tongue muscle segmentation may be used in surgical planning and treatment outcome analysis of tongue cancer patients, and in studies of normal subjects and subjects with speech and swallowing problems.     

MallesNet: A multi-object assistance based network for brachial plexus segmentation in ultrasound images

Ultrasound-guided injection is widely used to help anesthesiologists perform anesthesia in peripheral nerve blockade (PNB). However, it is a daunting task to accurately identify nerve structure in ultrasound images even for the experienced anesthesiologists. In this paper, a Multi-object assistance based Brachial Plexus Segmentation Network, named MallesNet, is proposed to improve the nerve segmentation performance in ultrasound image with the assistance of simultaneously segmenting its surrounding anatomical structures (e.g., muscle, vein, and artery). The MallesNet is designed by following the framework of Mask R-CNN to implement the multi object identification and segmentation. Moreover, a spatial local contrast feature (SLCF) extraction module is proposed to compute contrast features at different scales to effectively obtain useful features for small objects. And the self-attention gate (SAG) is also utilized to capture the spatial relationships in different channels and further re-weight the channels in feature maps by following the design of non-local operation and channel attention. Furthermore, the upsampling mechanism in original Feature Pyramid Network (FPN) is improved by adopting the transpose convolution and skip concatenation to fine-tune the feature maps. The Ultrasound Brachial Plexus Dataset (UBPD) is also proposed to support the research on brachial plexus segmentation, which consists of 1055 ultrasound images with four objects (i.e., nerve, artery, vein and muscle) and their corresponding label masks. Extensive experimental results using UBPD dataset demonstrate that MallesNet can achieve a better segmentation performance on nerves structure and also on surrounding structures in comparison to other competing approaches.     

基于MR模板的PET/MRI衰减校正方法

目的 设计并对比基于MRI的衰减校正模板对PET图像的衰减校正效果,寻找衰减校正效果最佳模板。方法 采集30名志愿者的¹⁸F-FDG PET图像,根据性别或不同头部尺寸创建MRI模板,包括女性组（fmg）、男性组（mmg）、小尺寸头部组（smg）、大尺寸头部组（bmg）和基础组（gmg）,与PET透射模板结合后获得个体化衰减图,并最终用于¹⁸F-FDG PET图像的衰减校正;测量并对比经不同模板校正的PET图像ROI内的放射性活度值和与原始PET图像相比的相对偏差值。结果 女性受试者经衰减校正的PET图像中,其ROI内放射性活度的最小差值为4 Bq/ml,来源于fmg与gmg模板间,而男性受试者放射性活度的最小差值为1 Bq/ml,来源于mmg与fmg模板间。经5个不同模板校正后与经PET透射扫描所获得的衰减数据之间均存在差异,相对偏差值范围为-6.31%~6.32%。gmg模板与其他4个模板的相对偏差值范围为-3.44%~5.34%。结论 基于MR模板的衰减校正方法对于受试者个体不存在明显的性别差异,一般情况下,gmg模板即可满足要求。     

基于V-Net卷积神经网络深度学习模型自动分割腰椎CT图像中的椎旁肌

目的 观察基于V-Net卷积神经网络(CNN)的深度学习(DL)模型自动分割腰椎CT图像中的椎旁肌的价值。方法 收集471例接受腰椎CT检查患者，按7∶3比例将其分为训练集(n=330)和测试集(n=141);采用2D V-Net进行训练，建立DL模型；观察其分割腰大肌、腰方肌、椎后肌群及椎旁肌的价值。结果 基于V-Net CNN的DL模型分割椎旁肌精度良好，戴斯相似系数(DSC)均较高、肌肉横截面积误差率(CSA error)均较低；其分割训练集图像中的腰大肌、腰方肌及椎旁肌的DSC均高于测试集(P均<0.05),而分割训练集中4组肌肉的CSA error均低于测试集(P均<0.05)。测试集内两两比较结果显示，该模型分割椎后肌群的DSC最高、腰方肌的DSC最低；分割腰方肌的CSA error最高、椎旁肌的CSA error最低(P均<0.05)。结论 以基于V-Net的DL模型自动分割椎旁肌的效能较佳。     

CMC-Net: 3D calf muscle compartment segmentation with sparse annotation

Accurate 3D segmentation of calf muscle compartments in volumetric MR images is essential to diagnose as well as assess progression of muscular diseases. Recently, good segmentation performance was achieved using state-of-the-art deep learning approaches, which, however, require large amounts of annotated data for training. Considering that obtaining sufficiently large medical image annotation datasets is often difficult, time-consuming, and requires expert knowledge, minimizing the necessary sizes of expert-annotated training datasets is of great importance. This paper reports CMC-Net, a new deep learning framework for calf muscle compartment segmentation in 3D MR images that selects an effective small subset of 2D slices from the 3D images to be labelled, while also utilizing unannotated slices to facilitate proper generalization of the subsequent training steps. Our model consists of three parts: (1) an unsupervised method to select the most representative 2D slices on which expert annotation is performed; (2) ensemble model training employing these annotated as well as additional unannotated 2D slices; (3) a model-tuning method using pseudo-labels generated by the ensemble model that results in a trained deep network capable of accurate 3D segmentations. Experiments on segmentation of calf muscle compartments in 3D MR images show that our new approach achieves good performance with very small annotation ratios, and when utilizing full annotation, it outperforms state-of-the-art full annotation segmentation methods. Additional experiments on a 3D MR thigh dataset further verify the ability of our method in segmenting leg muscle groups with sparse annotation.     

Extrastriatal Mean Regional Uptake of Fluorine-18-FDOPA in the Normal Aged Brain—An Approach Using MRI-Aided Spatial Normalization

The aim of this study was to define the mean regional 6-[¹⁸F]fluoro- -dopa (FDOPA) uptake rate constant (K_i) values in the striatal and extrastriatal regions of the brain of normal subjects using magnetic resonance imaging (MRI)-aided spatial normalization of the FDOPA K_i image and using automatic region of interest (ROI) analysis. Dynamic three-dimensional FDOPA positron emission tomography (PET) and three-dimensional magnetic resonance (MR) images were acquired in 13 aged normal subjects. The FDOPA add image and the K_i image of each subject were transformed into standard stereotactic space with the aid of individual coregistered MR image. The mean regional K_i values of the striatal and extrastriatal regions before normalization were compared with the respective values after normalization. Then automatic ROI analysis was performed on the MRI-aided spatially normalized K_i images of the 13 normal subjects. The K_i values on original images and those on spatially normalized images were in good agreement, indicating that the spatial normalization technique did not change the regional K_i values appreciably. Automatic ROI analysis of the spatially normalized FDOPA K_i images of the normal subjects, showed high K_i values in ventral and dorsal regions of the midbrain, amygdala, hippocampus, and medial prefrontal cortex, in addition to caudate nucleus and putamen, which correspond to the dopaminergic projections in the brain. Spatial normalization technique helped to establish a database of FDOPA K_i images of normal subjects and high K_i values were observed widely besides striatal regions corresponding to the dopaminergic projections in the brain.     

Automated muscle segmentation from CT images of the hip and thigh using a hierarchical multi-atlas method

Purpose

Patient-specific quantitative assessments of muscle mass and biomechanical musculoskeletal simulations require segmentation of the muscles from medical images. The objective of this work is to automate muscle segmentation from CT data of the hip and thigh.

Method

We propose a hierarchical multi-atlas method in which each hierarchy includes spatial normalization using simpler pre-segmented structures in order to reduce the inter-patient variability of more complex target structures.

Results

The proposed hierarchical method was evaluated with 19 muscles from 20 CT images of the hip and thigh using the manual segmentation by expert orthopedic surgeons as ground truth. The average symmetric surface distance was significantly reduced in the proposed method (1.53 mm) in comparison with the conventional method (2.65 mm).

Conclusion

We demonstrated that the proposed hierarchical multi-atlas method improved the accuracy of muscle segmentation from CT images, in which large inter-patient variability and insufficient contrast were involved.

     

模糊K-均值聚类算法及其在磁共振颅脑图像分割中的应用研究

目的　介绍一种动态模糊聚类算法并利用该算法对磁共振图像进行分割研究。方法　首先对磁共振颅脑图像进行预处理去掉颅骨和肌肉等非脑组织,只保留大脑组织,然后利用模糊Ｋ－ 均值聚类算法计算脑白质、脑灰质和脑脊液的模糊类属函数。结果　模糊Ｋ－ 均值聚类算法能很好地分割出磁共振颅脑图像中的灰质、白质和脑脊液。结论　利用模糊Ｋ－ 均值聚类算法分割磁共振颅脑图像能获得较好的分割效果。     

Segmentation of the mouse hippocampal formation in magnetic resonance images

The hippocampal formation plays an important role in cognition, spatial navigation, learning, and memory. High resolution magnetic resonance (MR) imaging makes it possible to study in vivo changes in the hippocampus over time and is useful for comparing hippocampal volume and structure in wild type and mutant mice. Such comparisons demand a reliable way to segment the hippocampal formation. We have developed a method for the systematic segmentation of the hippocampal formation using the perfusion-fixed C57BL/6 mouse brain for application in longitudinal and comparative studies. Our aim was to develop a guide for segmenting over 40 structures in an adult mouse brain using 30 μm isotropic resolution images acquired with a 16.4 T MR imaging system and combined using super-resolution reconstruction.     

Capsules for biomedical image segmentation

Our work expands the use of capsule networks to the task of object segmentation for the first time in the literature. This is made possible via the introduction of locally-constrained routing and transformation matrix sharing, which reduces the parameter/memory burden and allows for the segmentation of objects at large resolutions. To compensate for the loss of global information in constraining the routing, we propose the concept of “deconvolutional” capsules to create a deep encoder-decoder style network, called SegCaps. We extend the masked reconstruction regularization to the task of segmentation and perform thorough ablation experiments on each component of our method. The proposed convolutional-deconvolutional capsule network, SegCaps, shows state-of-the-art results while using a fraction of the parameters of popular segmentation networks. To validate our proposed method, we perform experiments segmenting pathological lungs from clinical and pre-clinical thoracic computed tomography (CT) scans and segmenting muscle and adipose (fat) tissue from magnetic resonance imaging (MRI) scans of human subjects’ thighs. Notably, our experiments in lung segmentation represent the largest-scale study in pathological lung segmentation in the literature, where we conduct experiments across five extremely challenging datasets, containing both clinical and pre-clinical subjects, and nearly 2000 computed-tomography scans. Our newly developed segmentation platform outperforms other methods across all datasets while utilizing less than 5% of the parameters in the popular U-Net for biomedical image segmentation. Further, we demonstrate capsules’ ability to generalize to unseen handling of rotations/reflections on natural images.     

Automated segmentation of basal ganglia and deep brain structures in MRI of Parkinson’s disease

Purpose

Template-based segmentation techniques have been developed to facilitate the accurate targeting of deep brain structures in patients with movement disorders. Three template-based brain MRI segmentation techniques were compared to determine the best strategy for segmenting the deep brain structures of patients with Parkinson’s disease.

Methods

T1-weighted and T2-weighted magnetic resonance (MR) image templates were created by averaging MR images of 57 patients with Parkinson’s disease. Twenty-four deep brain structures were manually segmented on the templates. To validate the template-based segmentation, 14 of the 24 deep brain structures from the templates were manually segmented on 10 MR scans of Parkinson’s patients as a gold standard. We compared the manual segmentations with three methods of automated segmentation: two registration-based approaches, automatic nonlinear image matching and anatomical labeling (ANIMAL) and symmetric image normalization (SyN), and one patch-label fusion technique. The automated labels were then compared with the manual labels using a Dice-kappa metric and center of gravity. A Friedman test was used to compare the Dice-kappa values and paired t tests for the center of gravity.

Results

The Friedman test showed a significant difference between the three methods for both thalami (p < 0.05) and not for the subthalamic nuclei. Registration with ANIMAL was better than with SyN for the left thalamus and was better than the patch-based method for the right thalamus.

Conclusion

Although template-based approaches are the most used techniques to segment basal ganglia by warping onto MR images, we found that the patch-based method provided similar results and was less time-consuming. Patch-based method may be preferable for the subthalamic nucleus segmentation in patients with Parkinson’s disease.     

Segmentation of lumen and outer wall of abdominal aortic aneurysms from 3D black-blood MRI with a registration based geodesic active contour model

Segmentation of the geometric morphology of abdominal aortic aneurysm is important for interventional planning. However, the segmentation of both the lumen and the outer wall of aneurysm in magnetic resonance (MR) image remains challenging. This study proposes a registration based segmentation methodology for efficiently segmenting MR images of abdominal aortic aneurysms. The proposed methodology first registers the contrast enhanced MR angiography (CE-MRA) and black-blood MR images, and then uses the Hough transform and geometric active contours to extract the vessel lumen by delineating the inner vessel wall directly from the CE-MRA. The proposed registration based geometric active contour is applied to black-blood MR images to generate the outer wall contour. The inner and outer vessel wall are then fused presenting the complete vessel lumen and wall segmentation. The results obtained from 19 cases showed that the proposed registration based geometric active contour model was efficient and comparable to manual segmentation and provided a high segmentation accuracy with an average Dice value reaching 89.79%.     

Characterization of a sequential pipeline approach to automatic tissue segmentation from brain MR Images

Objective Quantitative analysis of gray matter and white matter in brain magnetic resonance imaging (MRI) is valuable for neuroradiology and clinical practice. Submission of large collections of MRI scans to pipeline processing is increasingly important. We characterized this process and suggest several improvements. Materials and methods To investigate tissue segmentation from brain MR images through a sequential approach, a pipeline that consecutively executes denoising, skull/scalp removal, intensity inhomogeneity correction and intensity-based classification was developed. The denoising phase employs a 3D-extension of the Bayes–Shrink method. The inhomogeneity is corrected by an improvement of the Dawant et al.’s method with automatic generation of reference points. The N3 method has also been evaluated. Subsequently the brain tissue is segmented into cerebrospinal fluid, gray matter and white matter by a generalized Otsu thresholding technique. Intensive comparisons with other sequential or iterative methods have been carried out using simulated and real images. Results The sequential approach with judicious selection on the algorithm selection in each stage is not only advantageous in speed, but also can attain at least as accurate segmentation as iterative methods under a variety of noise or inhomogeneity levels. Conclusion A sequential approach to tissue segmentation, which consecutively executes the wavelet shrinkage denoising, scalp/skull removal, inhomogeneity correction and intensity-based classification was developed to automatically segment the brain tissue into CSF, GM and WM from brain MR images. This approach is advantageous in several common applications, compared with other pipeline methods.     

Deep dense multi-path neural network for prostate segmentation in magnetic resonance imaging

Purpose

We propose an approach of 3D convolutional neural network to segment the prostate in MR images.

Methods

A 3D deep dense multi-path convolutional neural network that follows the framework of the encoder–decoder design is proposed. The encoder is built based upon densely connected layers that learn the high-level feature representation of the prostate. The decoder interprets the features and predicts the whole prostate volume by utilizing a residual layout and grouped convolution. A set of sub-volumes of MR images, centered at the prostate, is generated and fed into the proposed network for training purpose. The performance of the proposed network is compared to previously reported approaches.

Results

Two independent datasets were employed to assess the proposed network. In quantitative evaluations, the proposed network achieved 95.11 and 89.01 Dice coefficients for the two datasets. The segmentation results were robust to variations in MR images. In comparison experiments, the segmentation performance of the proposed network was comparable to the previously reported approaches. In qualitative evaluations, the segmentation results by the proposed network were well matched to the ground truth provided by human experts.

Conclusions

The proposed network is capable of segmenting the prostate in an accurate and robust manner. This approach can be applied to other types of medical images.

     

Automatic cardiac ventricle segmentation in MR images: a validation study

Purpose  

Segmenting the cardiac ventricles in magnetic resonance (MR) images is required for cardiac function assessment. Numerous segmentation methods have been developed and applied to MR ventriculography. Quantitative validation of these segmentation methods with ground truth is needed prior to clinical use, but requires manual delineation of hundreds of images. We applied a well-established method to this problem and rigorously validated the results.     

Trismus originating from rare fungal myositis in pterygoid muscles: A case report

BACKGROUNDTrismus is a common problem with various causes. Any abnormal conditions of relevant anatomic structures that disturb the free movement of the jaw might provoke trismus. Trismus has a detrimental effect on the quality of life. The outcome of this abnormality is critically dependent on timely diagnosis and treatment, and it is difficult to identify the true origin in some cases. We present a rare case of trismus due to fungal myositis in the pterygoid muscle, excluding any other possible pathogenesis. CASE SUMMARYThe patient presented with a 2-mo history of restricted mouth opening. Computed tomography showed obvious enlargement of the left pterygoid muscles. Furthermore, the patient had trismus without obvious predisposing causes. The primary diagnosis was pterygoid myosarcoma. Consequently, lesionectomy of the left pterygoid muscle was performed. Intraoperative frozen biopsy implied the possibility of an uncommon infection. Postoperative pathologic examination confirmed myositis and necrosis in the pterygoid muscle. Fungi were detected in both muscle tissue and surrounding necrotic tissue. The patient recovered well with antifungal therapy and mouth opening exercises. The rarity of fungal myositis may be responsible for the misdiagnosis. Although the origin of pathogenic fungi is still unknown, we believe that both hematogenous spread and local invasion could be the most likely sources. To the best of our knowledge, this is the first case in the literature that reported fungal myositis in pterygoid muscles as the only reason that results in trismus.CONCLUSIONSurgeons should remain vigilant to the possibility of trismus originating from fungal myositis.     

Local/non-local regularized image segmentation using graph-cuts

Objective

Multispectral, multichannel, or time series image segmentation is important for image analysis in a wide range of applications. Regularization of the segmentation is commonly performed using local image information causing the segmented image to be locally smooth or piecewise constant. A new spatial regularization method, incorporating non-local information, was developed and tested.

Methods

Our spatial regularization method applies to feature space classification in multichannel images such as color images and MR image sequences. The spatial regularization involves local edge properties, region boundary minimization, as well as non-local similarities. The method is implemented in a discrete graph-cut setting allowing fast computations.

Results

The method was tested on multidimensional MRI recordings from human kidney and brain in addition to simulated MRI volumes.

Conclusion

The proposed method successfully segment regions with both smooth and complex non-smooth shapes with a minimum of user interaction.     

Liver segmentation for contrast-enhanced MR images using partitioned probabilistic model

Purpose  

Liver volume segmentation is important in computer assisted diagnosis and therapy planning of liver tumors. Manual segmentation is time-consuming, tedious and error prone, so automated methods are needed. Automatic segmentation of MR images is more challenging than for CT images, so a robust system was developed.     

Deformable models in medical image analysis: a survey

This article surveys deformable models, a promising and vigorously researched computer-assisted medical image analysis technique. Among model-based techniques, deformable models offer a unique and powerful approach to image analysis that combines geometry, physics and approximation theory. They have proven to be effective in segmenting, matching and tracking anatomic structures by exploiting (bottom-up) constraints derived from the image data together with (top-down) a priori knowledge about the location, size and shape of these structures. Deformable models are capable of accommodating the significant variability of biological structures over time and across different individuals. Furthermore, they support highly intuitive interaction mechanisms that, when necessary, allow medical scientists and practitioners to bring their expertise to bear on the model-based image interpretation task. This article reviews the rapidly expanding body of work on the development and application of deformable models to problems of fundamental importance in medical image analysis, including segmentation, shape representation, matching and motion tracking.