- 學位論文
以解剖知識與形態學方法作人體下肢磁振影像不同功能肌肉群之自動分類
Automatic segmentation of different functional groups of lower extremity muscle on MRI : Combination of mathematical morphology and anatomy knowledge methods
摘要
磁振造影的影像分割有許多臨床上的實際用途：輔助醫生的診斷及治療、使流行病學統計容易進行、或是用於各類醫學研究的資料分析。然而多數時候，影像分割仰賴人工操作，費時費力且難以標準化。肌肉是人體重要組成成份，之前對身體其他部份，如腦部的自動影像分割，已有不少成果，然而尚無肌肉磁振影像的自動分割研究。 本論文的目的是發展一套自動分割人體下肢磁振影像上不同功能肌肉群之工具，可用於日漸增加的肌肉組織相關的研究，例如追蹤肌萎縮患者之病情變化或是不同型態運動員的肌肉運用與表現。由於不同身體部位的肌肉解剖型態及其週邊結構差異很大，為了方便及因應本實驗室其他研究之需求，本研究選擇以骨盆腔至小腿之肌肉為研究對象。又因為不同肌肉的磁振影像特性極為相似，難以自動區分單一之肌肉，因此本研究僅以自動分割出不同功能肌肉群為研究標的。 論文中將骨盆至小腿的肌肉依相鄰及相似為原則，由上而下分成數段處理。藉由每個區段的解剖知識輔助，例如皮下脂肪、骨盆的腸骨、大小腿骨及肌肉間筋膜及膝蓋的軟骨等在磁振影像的訊號與肌肉相差較大，可用為分界。再利用數學形態學中的侵蝕、膨脹、斷開、閉合等方法，及其他影像處理技巧如區域成長和區域填充等演算法，將八種不同功能的肌肉群一一分類出來。此自動分類再與物理治療師以手動方式所做出的分類做比較。結果顯示肌肉自動分類的總合平均絕對誤差(百分比誤差) ± 標準差為1736.5 (13.5%) ± 1071.8 ml。其中以膝伸肌的490.2 (14.0%) ± 371.8 ml為最大絕對誤差，而以踝屈肌的41.3 (7.7%) ± 11.6 ml為最小絕對誤差。相對誤差則以髖屈肌的242.8 (24.4%) ± 140.2為最大，踝伸肌的153.1 (6.7%) ± 94.3為最小。 本論文使用型態學之方法輔以解剖知識，成功發展出一套可以自動分類出人體下肢磁振影像不同功能的肌肉群之工具，未來可以再改進其準確度並應用於身體的其他部位，如臟器脂肪自動定量，應可協助醫生的診斷及治療或是幫助相關的研究。
並列摘要
Segmentation of magnetic resonance (MR) images has numerous clinical applications: Being an auxiliary tool in diagnosis and treatment, making epidemiological statistics easier to carry on, being an important step in data analysis of all kinds of medical researches. However, most of the segmentation has been done manually or at most semi-automatically. The process is time- and energy-consuming and difficult to be standardized. Automatic segmentation algorithms of MR images for some of the body parts, such as the brain, have been developed with success. The muscular tissue is a major component of the human body, however, to our knowledge, no similar studies had been done on automatic muscle segmentation on MR images. The goal of this study is to develop an automatic segmentation scheme to correctly assign different functional muscle groups on MR images of the human lower extremities. We speculated that the results could be used in increasing muscle-tissue-related researches, such as the monitoring of muscle volume change over the time for the victims of muscular dystrophy diseases and investigation of the use and the performance of different muscles in athletes of various sport types. The grouping and the surrounding anatomic structures are quite different for muscles located at different body parts. For convenience and to support another research of our labs, we chose to target at lower extremities, muscles from pelvis to ankle, as the object of the study. Since the MR signals for all muscles are more or less similar, instead of singling out an individual muscle, our study focused on automatic classification of functional muscle groups. In the thesis, the lower extremities were first divided into several longitudinal anatomic segments based on the principles of proximity and anatomic similarity. Since the MR signals are quite different among bones, fascia, fat, and muscles, subcutaneous fat, ilium of the pelvis, femur, tibia, fibula, meniscus, and the muscular fascia are available as boundaries. Equipped by this knowledge, we applied mathematical morphological operations such as erosion, dilation, open, and close, and other image processing techniques such as regional growing and filling to designate muscle areas on an MR image as one of the eight muscle functional groups. The results of the automatic segmentation were then compared against the classification manually made by a physical therapist. We found that the average total absolute error (relative error) ± the standard deviation of our automatic segmentation is 1736.5 (13.5%) ± 1071.8 ml, with 490.2 (14.0%) ± 371.8 ml of the knee extensors as the largest absolute error and 41.3 (7.7%) ± 11.6 ml of the ankle flexors as the smallest. On the other hand, the largest relative error is hip flexors’ 242.8 (24.4%) ± 140.2 ml and the smallest the ankle extensors’ 153.1 (6.7%) ± 94.3 ml. The study presents that the combined mathematical morphology and human anatomy knowledge approach successfully divided muscles of lower extremity MR images into meaningful functional groups without human intervention. In the future, the accuracy of this method could be further improved by more sophisticated revision such as MR-atlas registration. Applications on other body parts and tissues such as abdominal visceral fat are under investigation. We expect the results of this and related studies to be helpful in body-composition-related researches and perhaps also in clinical diagnosis and treatment.