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Segmentation of tongue muscles from super-resolution magnetic resonance images

机译：从超分辨率磁共振图像中分割舌头肌肉

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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. (C) 2014 Elsevier B.V. All rights reserved.
机译：舌部解剖结构的成像和量化有助于手术计划，舌癌患者的术后康复，以及研究人类如何适应和学习呼吸，吞咽和说话的新策略以补偿疾病，医疗干预或老化。由于在漫长的成像时间内会出现呼吸和非自愿吞咽动作，因此无法在体内采集具有清晰可见舌肌的高分辨率三维（3D）磁共振（MR）图像。但是，图像重建的最新进展现在允许从正交图像集生成超分辨率3D MR图像，这些图像以高平面分辨率获取并使用超分辨率技术进行组合。据我们所知，本文提出了从MR图像自动进行舌肌分割的首次尝试。我们设计了一个包含十个超分辨率3D MR图像的数据库，其中舌肌和纵向下舌肌被手动分割并标有地标。我们演示了通过应用基于地标的博弈论框架（GTF）自动分割感兴趣的肌肉的可行性，该框架中集成了基于Haar样特征的地标检测器和基于最佳分配的形状表示。针对第二观察者执行的独立手动分割以及B样条和恶魔地图集方法对获得的分割结果进行了验证。分割性能导致第二观察者GTF，B样条图集和恶魔图集的平均Dice系数分别为85.3％，81.8％，78.8％和75.8％。所获得的分割精度水平表明，计算机化的舌肌分割可用于舌癌患者的手术计划和治疗结果分析，以及正常受试者以及有言语和吞咽问题的受试者的研究。 （C）2014 Elsevier B.V.保留所有权利。

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