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研究生：	陳柏安 Chen, Bo-An
論文名稱：	基於核磁共振影像之肩盂骨缺損電腦自動評估系統 Computer evaluation system of glenoid bone loss from MR image
指導教授：	孫永年 Sun, Yung-Nien
共同指導教授:	蘇維仁 Su, Wei-Jen
學位類別：	碩士 Master
系所名稱：	電機資訊學院 - 資訊工程學系 Department of Computer Science and Information Engineering
論文出版年：	2013
畢業學年度：	101
語文別：	英文
論文頁數：	57
中文關鍵詞：	肩關節不穩定、肩盂骨分割、肩盂骨缺損、三維影像對位、核磁共振影像
外文關鍵詞：	shoulder instability, glenoid bone segmentation, glenoid bone loss, volume registration, magnetic resonance imaging
相關次數：	點閱：96 下載：2
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肩盂骨的骨缺損常常在前肩關節不穩定的狀況下被發現，由於肩盂股骨缺損的程度將會影像到後續外科醫師治療的方式，所以一個準確且可靠得的肩盂骨缺損量測是必要的。本論文著重於利用三維核磁共振影像量測並分析肩關節的肩盂骨缺損的程度，提出一套完整的方法流程，其中包含了肩盂骨的三維模型分割與肩盂骨缺損的量測。在肩盂骨的三維模型分割中，我們會先從訓練影像中建立肩盂骨的三維形狀模型，並訓練模型形變時所需要的比重參數，讓最後形變的結果能夠最接近手動的分割結果。為了得到更準確的分割結果，本論文整合了兩組不同掃描角度的核磁共振影像，兩組影像先利用兩階段的對位方式整合在一起，最後再進行模型形變的流程，得到高準確性的分割結果，尤其是在肩盂骨關節面的分割結果，更是有顯著的改善。在肩盂骨缺損的量測上，會先將分割好的肩盂骨旋轉至預先制訂好的量測切面，利用切面與肩盂骨的部分交集輪廓，並採用最佳擬合圓法來計算骨缺損的程度。
在實驗中，六名前肩關節脫臼的病人與一名正常人皆在成大醫院放射科中接受肩部核磁共振的檢查，並利用本論文所提出的方法自動計算肩盂骨缺損的程度，並將自動量測的結果與手動量測的結果作比較，其比較結果顯示兩者擁有高度的一致性，兩者之間也都只有些許的誤差。因此，本論文所提出的方法能夠幫助臨床上外科醫師評估病人肩盂骨缺損的程度，並且有機會成為輔助醫師的一個準確且可靠的工具。

Bone loss of the glenoid is a common finding in anterior glenohumeral instability. This study investigates the quantification of glenoid bone loss in anterior shoulder using T1-weighted oblique 3D MR images. The automatic method proposed for this study includes 3D glenoid bone reconstruction and glenoid bone loss measurement. In the glenoid bone reconstruction, an initial glenoid shape model was established first and the energy weights were trained from manually traced segmentation examples during an pre-training step. In order to get an accurate glenoid bone, an integrated segmentation method was designed by using two MRI sets from different scanning views. The two images were aligned by a two-step registration method including coarse and fine registration steps. In the coarse registration step, the segmented sagittal glenoid shape is registered to the axial image volume. In the fine registration step, mutual information (MI) and artificial bee colony (ABC) algorithm are adopted to optimize the registration parameters. The resulting glenoid surface was further refined by a deformation process as the final step.

Six cases with anterior shoulder dislocation and one healthy case were used in the experiment of glenoid bone loss quantification. The glenoid reconstruction result and bone loss measurement by the proposed automatic method were compared to manual results by an expert. The results showed only a small deviation between the two measurements. Thus, the proposed measurement method has great potential to be an accurate and robust tool for glenoid evaluation from shoulder MRI.

摘要		I
ABSTRACT		II
ACKNOWLEDGEMENTS	IV
CONTENTS		V
LIST OF TABLES	VII
LIST OF FIGURES	VIII
CHAPTER 1 INTRODUCTION	1
1.1 Background and Motivation	1
1.2 Related Works	4
1.2.1 Glenoid bone loss measurement	4
1.2.2 Latarjet procedure	7
CHAPTER 2 MATERIALS	9
2.1 Patients	9
2.2 Magnetic Resonance	9
CHAPTER 3 INITIAL GLENOID MODEL TRAINING AND ESTABLISHMENT	11
3.1 Glenoid shape model establishment	12
3.1.1 Glenoid contours segmentation	13
3.1.2 Marching cube algorithm	13
3.1.3 Laplacian smoothing and downsampling	15
3.2 Weights training of the deformable model	17
3.2.1 Classification for vertices of the glenoid bone model	18
3.2.2 Estimation of initial weights by Taguchi method [19]	19
3.2.3 Refine weights by Powell’s Method	22
CHAPTER 4 MODEL-BASED SEGMENTATION FROM SHOULDER MRI	24
4.1 Initial model fitting to MRI volume data	24
4.2 Glenoid bone segmentation	26
4.3 Glenoid bone segmentation from two sets of shoulder MRI	29
4.3.1 Registration for two volume data	30
4.3.1.1 Coarse registration	31
4.3.1.2 Fine registration	32
4.3.2 Glenoid segmentation from two shoulder MRI sets	35
CHAPTER 5 GLENOID BONE LOSS MEASUREMENT	37
5.1 The determination of model view for bone loss measurement	37
5.2 Best-fitting circle method	39
CHAPTER 6 EXPERIMENTAL RESULTS	42
6.1 The effect of using three sets of weights for deformation	42
6.2 Accuracy in Glenoid Bone Reconstruction from Sagittal MRI	43
6.3 Accuracy in glenoid bone reconstruction from two MRI sets	46
6.4 Accuracy in Measurement of Glenoid Bone Loss	49
6.5 The measurement of glenoid bone loss by surface area method	51
CHAPTER 7 CONCLUSIONS AND FUTURE WORKS	53
7.1 Conclusions	53
7.2 Future Works	54
REFERENCES	55

                                    

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校內：2016-08-30公開
校外：2016-08-30公開

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