本文選題：前列腺分割 + MR　； 參考：《南方醫(yī)科大學》2017年碩士論文

【摘要】：前列腺炎、前列腺增生、前列腺癌等疾病在男性中越來越普遍,前列腺癌已經是全球范圍內男性第二位最常見的癌癥。磁共振(Magnetic Resonance,MR)成像技術能夠較好的顯示前列腺內在組織結構,對于前列腺疾病的分析與診斷具有重要的臨床意義。臨床上,前列腺的大小、形狀、相對周圍組織器官的位置信息是對前列腺疾病及其病理階段進行診斷和分析的重要前提,同時在前列腺切除術、放射治療中也起著關鍵性的指導作用。因此,準確分割前列腺是至關重要的。但在MR圖像中,由于成像技術的限制、前列腺結構本身的復雜性以及不同個體之間前列腺形狀、大小和紋理信息的差異性,前列腺分割一直是近些年的一個難點。目前基于計算機的MR前列腺圖像全自動分割算法分割精度不高,尚不能滿足臨床要求。目前比較常見的MR前列腺圖像分割算法主要有基于分類器的方法、基于參數形變模型的方法和基于多圖譜的分割方法等。基于分類器的分割算法很大程度上依賴于分類器和提取的特征的性能,且當數據分布不平衡時,容易導致分類精度急劇下降;基于形變模型的分割算法對初始輪廓的形狀和位置敏感,常易陷入局部極值,算法的魯棒性差、抗干擾性差;而基于多圖譜的分割方法利用手動分割精度高的優(yōu)勢,將分割問題轉化為配準問題。通過配準技術,它能有效的將手動勾畫好的前列腺圖譜的形狀先驗知識融入分割過程,很好的重現(xiàn)分割結果。因此,基于多圖譜的分割方法是近幾年來非常受關注的一種方法�；诙鄨D譜的分割算法主要包含三個步驟:配準、圖譜選擇和圖譜融合,其中,圖譜選擇和融合策略可以在一定程度上降低配準誤差對分割過程所帶來的影響,有效提高分割精度,是近些年來國內外學者研究的兩個關鍵點。本文首先對目前比較經典的圖譜融合策略進行了學習和研究,并在此基礎上提出了一種距離場融合算法。距離場融合算法不再是對標號圖像進行融合,而是對每個標號圖像對應的距離場圖像進行融合。與圖譜標號相比,距離場不但反映了圖像中任意像素點的標號信息,同時也包含了該像素點與目標邊界的相對位置關系。本文提出的距離場融合算法主要基于以下兩個假設:(1)假設MR樣本與其對應的距離場(DF)樣本分別位于兩個非線性流形上,每個樣本都可由其局部鄰域內的樣本線性表示;(2)在局部空間內,MR樣本與其對應的DF樣本之間近似為微分同胚映射�；谶@兩個假設,則可以把距離場融合過程中權重系數的求解問題轉化為用MR字典樣本來線性表示測試樣本時字典系數的求解問題。本文采用測試樣本自身鄰域內的字典樣本對其進行線性表示,并利用強調局部性的局部錨點嵌入算法(LAE)對字典系數進行求解,最后對相應的DF字典樣本進行線性組合來獲得每個測試樣本所對應的距離場,再經過加權平均和閾值處理,從而得到最終的分割結果。實驗中與目前國際上比較流行的標號融合算法(Major Voting、SIMPLE、STAPLE、Nonlocal Patch-based Label Fusion等)進行了對比,實驗結果驗證了距離場融合算法的有效性;其次,針對配準誤差過大導致的分割效果較差的情況,本文引入了一種橢球形狀先驗,將多圖譜分割與橢球形狀先驗相結合,提出了一種橢球先驗約束的多圖譜MR前列腺分割算法。橢球先驗的引入,可以只針對橢球先驗約束下的前列腺感興趣區(qū)域進行圖譜選擇,避免前列腺周圍組織與器官對圖譜選擇造成的干擾。同時,在圖譜融合過程中加入橢球先驗項進行約束,可以對通過配準技術引入的前列腺圖譜形狀先驗進行校正和補償,避免由配準誤差引起的錯誤分割的情況。對50例MR前列腺圖像進行分割實驗,實驗結果表明該算法對前列腺數據的分割結果Dice相似度均在80%以上,平均Dice相似度提高到了 88.27%。
[Abstract]:Prostatitis, prostatic hyperplasia and prostate cancer are becoming more and more common in men. Prostate cancer is the second most common cancer in the world. Magnetic Resonance (MR) imaging technology can better display the internal structure of the prostate, which is important for the analysis and diagnosis of prostate disease. Clinically, the size, shape, and location of the prostate is an important prerequisite for the diagnosis and analysis of the prostate disease and its pathological stage, and it also plays a key role in the prostatectomy and radiation therapy. Therefore, it is very important to segment the prostate accurately. However, the MR image is very important. Because of the limitation of imaging technology, the complexity of prostate structure itself and the difference of prostate shape, size and texture information between different individuals, the segmentation of the prostate has been a difficult point in recent years. At present, the accuracy of the automatic segmentation of MR prostate image based on computer is not high, and the clinical requirements can not be met. The common MR prostate image segmentation algorithms are mainly based on classifier based methods, based on parameter deformation model and multi map based segmentation. The segmentation algorithm based on classifier depends largely on the performance of classifier and extracted features, and it is easy to lead to classification precision when the data distribution is unbalanced. The segmentation algorithm based on the deformation model is sensitive to the shape and position of the initial contour, is often prone to fall into the local extremum, the algorithm is poor in robustness and poor in anti-interference, and the segmentation method based on multi map uses the advantage of high precision of manual segmentation to transform the segmentation problem into registration problem. By registration technology, it can effectively hand the hand The shape prior knowledge of the shape of the prostate map is integrated into the segmentation process, and the segmentation results are reproduced well. Therefore, the segmentation method based on multi map is a very popular method in recent years. The segmentation algorithm based on multi map mainly contains three steps: registration, graph selection and map fusion, among which, map selection and fusion The strategy can reduce the effect of registration error to the segmentation process to a certain extent and improve the segmentation accuracy effectively. It is the two key point of scholars at home and abroad in recent years. Firstly, this paper studies and studies the classical map fusion strategy. On this basis, a distance field fusion algorithm is proposed. The field fusion algorithm is no longer the fusion of the label image, but the fusion of the distance field images corresponding to each label image. Compared with the map label, the distance field not only reflects the label information of any pixel in the image, but also contains the relative position relation between the pixel points and the boundary of the target. The algorithm is based on the following two hypotheses: (1) assuming that the MR sample and its corresponding distance field (DF) samples are located on two nonlinear manifolds, each sample can be linearly represented by the sample in its local neighborhood; (2) in the local space, the MR sample and its corresponding DF sample are approximately differential homeomorphic mapping. Based on these two hypotheses, The problem of solving the coefficient of the weight coefficient in the distance field fusion process can be transformed into the dictionary coefficient for the linear representation of the test sample with the MR dictionary sample. This paper uses the dictionary sample in the test sample's own neighborhood to express it linearly, and uses the local anchored point embedding algorithm (LAE) to the dictionary coefficient. Finally, the corresponding DF dictionary samples are linearly combined to obtain the distance field corresponding to each test sample, and then the weighted average and threshold processing is used to get the final segmentation results. In the experiment, the Major Voting, SIMPLE, STAPLE, Nonlocal Patch-based Label F are more popular in the experiment. Usion and so on, the experimental results verify the effectiveness of the distance field fusion algorithm. Secondly, in the case of poor registration error caused by too large registration error, this paper introduces a type of ellipsoid shape prior, combining the multi map segmentation with the ellipsoid shape prior, and proposes a multi map MR prostate segmentation with ellipsoid prior constraints. The introduction of ellipsoid prior can only select the map of the region of interest of the prostate under the ellipsoid prior constraint, avoid the interference of the tissues and organs around the prostate to the selection of the atlas. At the same time, the ellipsoid prior item is added to the map fusion process to restrict the prostate atlas, which is introduced by the registration technique. The shape prior is corrected and compensated to avoid the error segmentation caused by registration error. 50 cases of MR prostate images are segmented. The experimental results show that the Dice similarity of the proposed algorithm is more than 80% for the segmentation results of the prostate data, and the average Dice similarity is increased to 88.27%.
【學位授予單位】：南方醫(yī)科大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：R737.25;R445.2;TP391.41

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本文編號：1979278