基于耦合可變模型的膀胱壁分割方法
發(fā)布時間：2019-01-03 19:33
【摘要】：在膀胱磁共振(Magnetic Resonance, MR)圖像中，膀胱壁的準(zhǔn)確分割對臨床應(yīng)用和醫(yī)學(xué)研究具有重要意義。膀胱癌是一種發(fā)病率和復(fù)發(fā)率都很高的疾病，早期的檢測非常重要。膀胱內(nèi)外壁之間的厚度的異常增加，可以作為膀胱腫瘤檢測的一個重要指標(biāo)，而衡量膀胱壁厚度的一個最基本且最重要的工作是對內(nèi)外壁進(jìn)行準(zhǔn)確地分割。目前在臨床應(yīng)用中，通常是醫(yī)務(wù)人員手動地對其分割。但這是一項(xiàng)繁重而且耗時的工作，尤其是隨著醫(yī)學(xué)影像技術(shù)的發(fā)展，需要處理的圖像越來越多，只依靠手動分割是不可能完成的。因此，本文研究課題為高精度的計算機(jī)自動膀胱壁分割方法。 本文針對MR圖像的膀胱壁分割的挑戰(zhàn)，逐步研究并提出有效的解決方法。首先，針對膀胱MR圖像中存在的偽影問題，利用梯度的方向信息提出基于方向性梯度的水平集模型用以區(qū)分膀胱內(nèi)壁和偽影邊緣，在一定程度上減少了偽影對內(nèi)壁分割的影響；針對復(fù)雜的外部組織，利用膀胱壁的區(qū)域信息構(gòu)建出耦合水平集模型同時分割內(nèi)外壁，利用較準(zhǔn)確的內(nèi)壁分割修正外壁分割；并且在耦合水平集模型中加入最小壁厚度的先驗(yàn)知識，防止內(nèi)外零水平集的重疊或交叉。然后，針對靠近膀胱頂部或底部的層中存在的部分弱邊界的問題，本文提出利用上一層的分割結(jié)果作為形狀先驗(yàn)，并自適應(yīng)地約束本層的分割，，初步解決了水平集在弱邊界的泄漏問題。在驗(yàn)證了形狀先驗(yàn)對于膀胱壁分割的有效性之后，進(jìn)一步提出了更為準(zhǔn)確的形狀先驗(yàn)構(gòu)建方法，即部分稀疏形狀模型，利用部分可靠的輪廓構(gòu)建出完整可靠的形狀先驗(yàn)；并且提出了扇區(qū)驅(qū)動的水平集模型，更為全面的考慮了不同區(qū)域和不同演化階段對約束力的需求。最后，將本文所提出的部分稀疏形狀模型拓展到經(jīng)典的主動形狀模型(Active Shape Model, ASM)中，解決了由于部分弱邊界造成的錯誤搜索的問題，證明了該模型的普適性與有效性。 本文的主要創(chuàng)新點(diǎn)：1)提出耦合方向性水平集模型；2)提出自適應(yīng)形狀約束的水平集模型；3)提出部分稀疏形狀約束的扇區(qū)驅(qū)動的水平集模型；4)將部分稀疏形狀模型推廣到ASM中。我們的方法在15組不同病人的共167層的數(shù)據(jù)上進(jìn)行實(shí)驗(yàn)，膀胱壁的分割精度達(dá)到：內(nèi)壁的P2C值為1.06±0.28mm，DSC值為0.98±0.01，外壁的P2C值為1.46±0.42mm，DSC值為0.97±0.01，與現(xiàn)有方法對比，證明了本文所提出的方法的有效性與準(zhǔn)確性。
[Abstract]:In (Magnetic Resonance, MR) images of bladder, accurate segmentation of bladder wall is very important for clinical application and medical research. Bladder cancer is a disease with high incidence and recurrence rate. Early detection is very important. The abnormal increase of the thickness between the inner and outer walls of the bladder can be regarded as an important index for the detection of bladder tumor. The most basic and important work to measure the thickness of the bladder wall is to segment the inner and outer wall accurately. At present, in the clinical application, it is usually the medical personnel to divide it manually. But this is a heavy and time-consuming task, especially with the development of medical image technology, more and more images need to be processed. Therefore, a high-precision automatic bladder wall segmentation method is studied in this paper. Aiming at the challenge of bladder wall segmentation in MR image, this paper studies and proposes an effective solution step by step. Firstly, aiming at the artifact problem in bladder MR image, a level set model based on directional gradient is proposed to distinguish bladder inner wall from artifact edge, which reduces the influence of artifact on inner wall segmentation to a certain extent. For the complex external tissue, the coupled level set model was constructed to segment the inner and outer wall simultaneously, and the inner wall was used to segment the outer wall. A priori knowledge of minimum wall thickness is added to the coupled level set model to prevent the overlap or crossover of the internal and external zero level sets. Then, aiming at the problem of partial weak boundary in the layer near the top or bottom of the bladder, this paper proposes to use the segmentation result of the upper layer as a shape priori, and adaptively constrains the segmentation of the layer. The leakage problem of the level set at the weak boundary is solved preliminarily. After validating the validity of shape priori for bladder wall segmentation, a more accurate shape priori construction method, that is, partial sparse shape model, is proposed, and a complete and reliable shape priori is constructed by using partially reliable contour. A sector-driven level set model is proposed, which takes into account the binding requirements of different regions and different evolution stages. Finally, the partially sparse shape model proposed in this paper is extended to the classical active shape model (Active Shape Model, ASM), which solves the problem of error search caused by partial weak boundary, and proves the universality and validity of the model. The main innovations of this paper are as follows: 1) the coupling directional level set model is proposed; 2) the level set model with adaptive shape constraints is proposed; 3) the sector driven level set model with partial sparse shape constraints is proposed; 4) the partial sparse shape model is extended to ASM. Our method was tested on 167 layers of data from 15 different groups of patients. The division accuracy of bladder wall was 1.06 鹵0.28 mm DSC 0.98 鹵0.01 and 1.46 鹵0.42 mm respectively, and that of internal wall was 1.06 鹵0.28 mm DSC value was 0.98 鹵0.01, and that of outer wall was 1.46 鹵0.42 mm. The DSC value is 0. 97 鹵0. 01, which proves the validity and accuracy of the proposed method.
【學(xué)位授予單位】：中國科學(xué)院研究生院（西安光學(xué)精密機(jī)械研究所）
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：R445.2;R737.14
本文編號：2399785
[Abstract]:In (Magnetic Resonance, MR) images of bladder, accurate segmentation of bladder wall is very important for clinical application and medical research. Bladder cancer is a disease with high incidence and recurrence rate. Early detection is very important. The abnormal increase of the thickness between the inner and outer walls of the bladder can be regarded as an important index for the detection of bladder tumor. The most basic and important work to measure the thickness of the bladder wall is to segment the inner and outer wall accurately. At present, in the clinical application, it is usually the medical personnel to divide it manually. But this is a heavy and time-consuming task, especially with the development of medical image technology, more and more images need to be processed. Therefore, a high-precision automatic bladder wall segmentation method is studied in this paper. Aiming at the challenge of bladder wall segmentation in MR image, this paper studies and proposes an effective solution step by step. Firstly, aiming at the artifact problem in bladder MR image, a level set model based on directional gradient is proposed to distinguish bladder inner wall from artifact edge, which reduces the influence of artifact on inner wall segmentation to a certain extent. For the complex external tissue, the coupled level set model was constructed to segment the inner and outer wall simultaneously, and the inner wall was used to segment the outer wall. A priori knowledge of minimum wall thickness is added to the coupled level set model to prevent the overlap or crossover of the internal and external zero level sets. Then, aiming at the problem of partial weak boundary in the layer near the top or bottom of the bladder, this paper proposes to use the segmentation result of the upper layer as a shape priori, and adaptively constrains the segmentation of the layer. The leakage problem of the level set at the weak boundary is solved preliminarily. After validating the validity of shape priori for bladder wall segmentation, a more accurate shape priori construction method, that is, partial sparse shape model, is proposed, and a complete and reliable shape priori is constructed by using partially reliable contour. A sector-driven level set model is proposed, which takes into account the binding requirements of different regions and different evolution stages. Finally, the partially sparse shape model proposed in this paper is extended to the classical active shape model (Active Shape Model, ASM), which solves the problem of error search caused by partial weak boundary, and proves the universality and validity of the model. The main innovations of this paper are as follows: 1) the coupling directional level set model is proposed; 2) the level set model with adaptive shape constraints is proposed; 3) the sector driven level set model with partial sparse shape constraints is proposed; 4) the partial sparse shape model is extended to ASM. Our method was tested on 167 layers of data from 15 different groups of patients. The division accuracy of bladder wall was 1.06 鹵0.28 mm DSC 0.98 鹵0.01 and 1.46 鹵0.42 mm respectively, and that of internal wall was 1.06 鹵0.28 mm DSC value was 0.98 鹵0.01, and that of outer wall was 1.46 鹵0.42 mm. The DSC value is 0. 97 鹵0. 01, which proves the validity and accuracy of the proposed method.
【學(xué)位授予單位】：中國科學(xué)院研究生院（西安光學(xué)精密機(jī)械研究所）
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：R445.2;R737.14
【參考文獻(xiàn)】
相關(guān)期刊論文 前1條
1 段儕杰;田珍;梁正榮;包尚聯(lián);袁克虹;;磁共振虛擬膀胱鏡中膀胱壁分割與壁厚估算[J];清華大學(xué)學(xué)報(自然科學(xué)版);2010年09期
本文編號：2399785
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