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1.
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.  相似文献   

2.
《Medical image analysis》2015,21(1):198-207
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.  相似文献   

3.
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.  相似文献   

4.
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。  相似文献   

5.
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。  相似文献   

6.
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.  相似文献   

7.
The aim of this study was to define the mean regional 6-[18F]fluoro- -dopa (FDOPA) uptake rate constant (Ki) values in the striatal and extrastriatal regions of the brain of normal subjects using magnetic resonance imaging (MRI)-aided spatial normalization of the FDOPA Ki 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 Ki image of each subject were transformed into standard stereotactic space with the aid of individual coregistered MR image. The mean regional Ki 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 Ki images of the 13 normal subjects. The Ki 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 Ki values appreciably. Automatic ROI analysis of the spatially normalized FDOPA Ki images of the normal subjects, showed high Ki 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 Ki images of normal subjects and high Ki values were observed widely besides striatal regions corresponding to the dopaminergic projections in the brain.  相似文献   

8.
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。  相似文献   

9.

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.
  相似文献   

10.
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.  相似文献   

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