本文選題：肺4D-CT　切入點(diǎn)：圖像分割　出處：《南方醫(yī)科大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：肺癌是常見的惡性腫瘤之一,嚴(yán)重威脅人類健康,而放射治療是治療肺癌的有效手段。肺4D-CT在肺癌的放射治療中扮演著重要角色,它能真實(shí)地反映人體各器官組織和腫瘤隨呼吸運(yùn)動的變化規(guī)律,這些信息對于肺癌的精確放療意義重大。利用肺4D-CT圖像可以針對患者的運(yùn)動特征制定個體化的精確放療計劃,降低靶區(qū)外放邊界,提高靶區(qū)受照射劑量,減少周圍正常器官組織所受的照射劑量。但是一套肺4D-CT數(shù)據(jù)是由多個相位的圖像組成,通常包含上千張圖片,醫(yī)生勾畫靶區(qū)的工作量就成倍增長,這會影響醫(yī)生對靶區(qū)的準(zhǔn)確判斷。而在放療過程中靶區(qū)定位十分關(guān)鍵,其準(zhǔn)確性直接影響放療效果,因此肺4D-CT的靶區(qū)勾畫是一個耗時耗力的工作。利用圖像分割技術(shù)來幫助醫(yī)生勾畫靶區(qū)就是有效的解決辦法。圖像分割是根據(jù)人們的需要,將圖像劃分成各具特性的不同區(qū)域。醫(yī)學(xué)圖像分割就是將感興趣的器官或組織提取出來。本文就是對肺4D-CT圖像進(jìn)行分割,提取出各個相位上的腫瘤圖像,幫助醫(yī)生確定靶區(qū)。雖然近年來圖像分割算法層出不窮,但是CT圖像上腫瘤與周圍組織的對比度較低,而且肺腫瘤大小形態(tài)位置各異,容易侵犯周圍正常組織器官而發(fā)生粘連,邊界模糊,準(zhǔn)確分割肺腫瘤圖像依然十分富有挑戰(zhàn)性。而且從降低醫(yī)生工作負(fù)擔(dān)的角度來說,對分割算法的自動化程度也要求較高。本文針對上述問題,提出了兩種基于不同約束圖割的肺4D-CT圖像分割算法,是在圖割算法的基礎(chǔ)上對其進(jìn)行了改進(jìn),提高了它的分割精度和自動化程度,協(xié)助醫(yī)生勾畫靶區(qū),降低他們的工作量,使4D-CT更方便地應(yīng)用于肺腫瘤精確放療。其一,本文提出了一種基于星形先驗(yàn)和圖割的肺4D-CT腫瘤自動分割方法。首先在4D-CT初始相位圖像上,由醫(yī)生選取目標(biāo)種子點(diǎn),以此種子點(diǎn)為中心,形成一個N×N×N大小的初始目標(biāo)塊,將腫瘤全部包含在其中。N的大小由醫(yī)生觀測腫瘤大小估計得到。然后采用運(yùn)動估計中的完全搜索塊匹配算法,獲得下一相位圖像中與初始目標(biāo)塊最相似的目標(biāo)塊,同時估計出它們之間的運(yùn)動位移,以此類推,可以得到所有相位的目標(biāo)塊以及對應(yīng)塊之間的運(yùn)動位移。接下來利用這些運(yùn)動位移和初始相位目標(biāo)種子點(diǎn)的位置,計算出其余各相位目標(biāo)種子點(diǎn),作為星形先驗(yàn)的中心點(diǎn)。最后在各相位的目標(biāo)塊上使用結(jié)合星形先驗(yàn)的圖割算法,即可得到腫瘤分割結(jié)果。實(shí)驗(yàn)結(jié)果表明,此方法的分割準(zhǔn)確性優(yōu)于傳統(tǒng)圖割算法,同時也提升了算法的自動化程度。其二,本文提出了一種基于圖割的利用肺4D-CT上下文信息的多相位腫瘤聯(lián)合分割方法。該方法是將肺4D-CT各相位圖像聯(lián)合構(gòu)建成一個全局網(wǎng)絡(luò)圖,每個相位的圖像都是一個子網(wǎng)絡(luò)圖,上下文信息作為約束項加入到各個子網(wǎng)絡(luò)圖之間,也就是給相鄰網(wǎng)絡(luò)圖的對應(yīng)節(jié)點(diǎn)之間加上邊。在多相位網(wǎng)絡(luò)圖的基礎(chǔ)上,我們構(gòu)建了新的全局能量函數(shù),包含各相位的區(qū)域項、邊界項和新增加的上下文信息約束項。其中區(qū)域項和邊界項的構(gòu)造與原始圖割算法相同,而上下文信息約束項參考了 Potts模型,用來懲罰相鄰相位對應(yīng)體素點(diǎn)標(biāo)號不一致的情況。通過優(yōu)化新的能量函數(shù)就能完成多相位腫瘤的聯(lián)合自動分割。該方法進(jìn)一步減少了用戶交互,用戶只需在某一相位圖像上選取目標(biāo)和背景種子點(diǎn),所有相位上的腫瘤都能被自動分割出來。我們在十套肺4D-CT數(shù)據(jù)上進(jìn)行了實(shí)驗(yàn),從視覺和量化結(jié)果來看,該方法的分割結(jié)果優(yōu)于未加入上下文約束的圖割算法以及結(jié)合星型先驗(yàn)的圖割算法。
[Abstract]:Lung cancer is one of the most common malignant tumor, a serious threat to human health, and radiation therapy is an effective therapy for lung cancer. Lung radiotherapy plays an important role in 4D-CT lung cancer, it can reflect the human organs and tumor tissue changes with the respiratory motion, precise radiotherapy for lung cancer and the significance of these information. The plan of precise radiotherapy for lung 4D-CT images can be individualized according to the motion characteristics of the patients, reduce the target area on the outside of the boundary, improve the radiation dose target area, reduce the radiation dose to surrounding normal organs. But by a set of lung 4D-CT data is composed of a plurality of phase images, usually contains thousands of pictures the doctor, target delineation of the workload doubled, it will affect the doctor to target accurately. In the course of radiotherapy target positioning is very important, its accuracy directly affects the radiotherapy Therefore, the target delineation of lung 4D-CT is a time-consuming work. Segmentation technology to help doctors target delineation is an effective solution using image. Image segmentation is according to the needs of the people, will be divided into different areas of the image. The characteristics of the medical image segmentation is of interest to the organ or tissue this article is extracted. The segmentation of lung 4D-CT images, extract tumor image of each phase, help the doctor determine the target area. Although in recent years, the image segmentation algorithm of CT image but emerge in an endless stream, the tumor and surrounding tissue of low contrast and lung tumor size and shape at different locations, easy invasion of surrounding normal organs and tissues adhesion, fuzzy boundaries, accurate segmentation of lung tumor image is still very challenging. But from the perspective of reducing the doctors work burden, the degree of automation of the segmentation algorithm Also higher. Aiming at these problems, put forward two kinds of segmentation algorithms based on different constraint graph cut lung 4D-CT images, is based on the graph cut algorithm on its improvement, improve its segmentation accuracy and automation, to assist doctors to target delineation, reduce their workload, make the 4D-CT more convenient application in lung tumor precise radiotherapy. First, this paper presents a method for automatic segmentation of Hoshi Gata's transcendental and graph cut based on 4D-CT lung cancer. First in the initial phase of 4D-CT image, by the doctor to select the target seed point, the seed point as the center, to form the initial target block of a N * N * N size all included in the tumor, which the size of the.N by the observation of tumor size. Then the doctor estimated full search motion estimation block matching algorithm, to obtain the next phase in the image and the initial target block is most similar to the target block, at the same time The estimated motion between them and so on, can get all of the target block and the corresponding block phase between the displacement. Then using the displacement and the initial phase of target seed location, calculate the rest of the phase of target seed point as the center point of star shape prior. The last target block in each phase of combined with the use of a priori star graph cut algorithm, the segmentation results of tumor can be obtained. The experimental results show that the segmentation accuracy of this method is better than the traditional graph cut algorithm, but also enhance the degree of automation of the algorithm. Secondly, this paper proposes a segmentation method combined with multi phase 4D-CT lung tumor using context information based on the graph cut. The 4D-CT method is the pulmonary phase image are combined to construct into a global network map, the image of each phase is a sub network, context information as a constraint and Into each sub network diagram, is to add on the corresponding node between adjacent network diagram. Based on multi phase network graph, we construct the global new energy function, including the phase boundary region, and additional context information. The regional tectonic constraints and boundary the same with the original graph cut algorithm, and context information constraints refer to the Potts model, to punish the adjacent phase corresponding voxel labeling isinconsistent. Combined with automatic segmentation of multi phase tumor by optimizing energy function. The new method can further reduce the user interaction, the user only need to select the object and background seeds in a phase image, all phase of the tumor can be automatically separated. We conducted experiments on ten sets of lung 4D-CT data, from the visual and quantitative results, the method of The cutting results are better than that of the graph cut algorithm that does not join the context constraint and the graph cutting algorithm combined with the star type prior.

【學(xué)位授予單位】：南方醫(yī)科大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R734.2;TP391.41

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