發(fā)布時(shí)間：2018-06-04 23:17

  本文選題：前列腺分割 + MR��； 參考：《南方醫(yī)科大學(xué)》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(jīng)是全球范圍內(nèi)男性第二位最常見的癌癥。磁共振(Magnetic Resonance,MR)成像技術(shù)能夠較好的顯示前列腺內(nèi)在組織結(jié)構(gòu),對(duì)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(duì)周圍組織器官的位置信息是對(duì)前列腺疾病及其病理階段進(jìn)行診斷和分析的重要前提,同時(shí)在前列腺切除術(shù)、放射治療中也起著關(guān)鍵性的指導(dǎo)作用。因此,準(zhǔn)確分割前列腺是至關(guān)重要的。但在MR圖像中,由于成像技術(shù)的限制、前列腺結(jié)構(gòu)本身的復(fù)雜性以及不同個(gè)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(gè)難點(diǎn)。目前基于計(jì)算機(jī)的MR前列腺圖像全自動(dòng)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(shù)形變模型的方法和基于多圖譜的分割方法等�；诜诸惼鞯姆指钏惴ê艽蟪潭壬弦蕾囉诜诸惼骱吞崛〉奶卣鞯男阅�,且當(dāng)數(shù)據(jù)分布不平衡時(shí),容易導(dǎo)致分類精度急劇下降;基于形變模型的分割算法對(duì)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(dòng)分割精度高的優(yōu)勢,將分割問題轉(zhuǎn)化為配準(zhǔn)問題。通過配準(zhǔn)技術(shù),它能有效的將手動(dòng)勾畫好的前列腺圖譜的形狀先驗(yàn)知識(shí)融入分割過程,很好的重現(xiàn)分割結(jié)果。因此,基于多圖譜的分割方法是近幾年來非常受關(guān)注的一種方法�；诙鄨D譜的分割算法主要包含三個(gè)步驟:配準(zhǔn)、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(zhǔn)誤差對(duì)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(nèi)外學(xué)者研究的兩個(gè)關(guān)鍵點(diǎn)。本文首先對(duì)目前比較經(jīng)典的圖譜融合策略進(jìn)行了學(xué)習(xí)和研究,并在此基礎(chǔ)上提出了一種距離場融合算法。距離場融合算法不再是對(duì)標(biāo)號(hào)圖像進(jìn)行融合,而是對(duì)每個(gè)標(biāo)號(hào)圖像對(duì)應(yīng)的距離場圖像進(jìn)行融合。與圖譜標(biāo)號(hào)相比,距離場不但反映了圖像中任意像素點(diǎn)的標(biāo)號(hào)信息,同時(shí)也包含了該像素點(diǎn)與目標(biāo)邊界的相對(duì)位置關(guān)系。本文提出的距離場融合算法主要基于以下兩個(gè)假設(shè):(1)假設(shè)MR樣本與其對(duì)應(yīng)的距離場(DF)樣本分別位于兩個(gè)非線性流形上,每個(gè)樣本都可由其局部鄰域內(nèi)的樣本線性表示;(2)在局部空間內(nèi),MR樣本與其對(duì)應(yīng)的DF樣本之間近似為微分同胚映射�；谶@兩個(gè)假設(shè),則可以把距離場融合過程中權(quán)重系數(shù)的求解問題轉(zhuǎn)化為用MR字典樣本來線性表示測試樣本時(shí)字典系數(shù)的求解問題。本文采用測試樣本自身鄰域內(nèi)的字典樣本對(duì)其進(jìn)行線性表示,并利用強(qiáng)調(diào)局部性的局部錨點(diǎn)嵌入算法(LAE)對(duì)字典系數(shù)進(jìn)行求解,最后對(duì)相應(yīng)的DF字典樣本進(jìn)行線性組合來獲得每個(gè)測試樣本所對(duì)應(yīng)的距離場,再經(jīng)過加權(quán)平均和閾值處理,從而得到最終的分割結(jié)果。實(shí)驗(yàn)中與目前國際上比較流行的標(biāo)號(hào)融合算法(Major Voting、SIMPLE、STAPLE、Nonlocal Patch-based Label Fusion等)進(jìn)行了對(duì)比,實(shí)驗(yàn)結(jié)果驗(yàn)證了距離場融合算法的有效性;其次,針對(duì)配準(zhǔn)誤差過大導(dǎo)致的分割效果較差的情況,本文引入了一種橢球形狀先驗(yàn),將多圖譜分割與橢球形狀先驗(yàn)相結(jié)合,提出了一種橢球先驗(yàn)約束的多圖譜MR前列腺分割算法。橢球先驗(yàn)的引入,可以只針對(duì)橢球先驗(yàn)約束下的前列腺感興趣區(qū)域進(jìn)行圖譜選擇,避免前列腺周圍組織與器官對(duì)圖譜選擇造成的干擾。同時(shí),在圖譜融合過程中加入橢球先驗(yàn)項(xiàng)進(jìn)行約束,可以對(duì)通過配準(zhǔn)技術(shù)引入的前列腺圖譜形狀先驗(yàn)進(jìn)行校正和補(bǔ)償,避免由配準(zhǔn)誤差引起的錯(cuò)誤分割的情況。對(duì)50例MR前列腺圖像進(jìn)行分割實(shí)驗(yàn),實(shí)驗(yàn)結(jié)果表明該算法對(duì)前列腺數(shù)據(jù)的分割結(jié)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(xué)位授予單位】：南方醫(yī)科大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：R737.25;R445.2;TP391.41

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本文編號(hào)：1979278