動態乳房彈性超音波之腫瘤偵測與分析

Abstract

近年來，乳房彈性超音波已成為測量腫瘤彈性最常見的方法，醫生必須施以輕微的壓力於腫瘤組織之上，藉以得到一連串連續的動態彈力影像。而醫生將會從這一序列的影像中挑選出最具代表性的一張影像，此張影像之品質優劣亦將決定診斷的準確性。此篇論文之目的為量化彈力超音波影像的品質並選出彈力超音波影片中最具代表性的一張影像，同時也將實作一可半自動切割腫瘤以利計算腫瘤的彈性比率。利用使用者在首張影像所選取的種子點，配合邊緣偵測(Edge Detection)以及區域增長(Region Growing)的方法可自動地切出整個資料中的腫瘤輪廓，此外，種子點會根據之前影像中腫瘤移動的方式做相同的位移，如此方可得到較好的切割結果。根據所得的腫瘤輪廓中的一致性以及腫瘤跟正常組織的對比性，可以量化兩種彈力影像品質：信號雜訊比（SNRe）以及對比雜訊比（CNRe），並根據量化結果挑選出最具代表性之影像作為判斷良惡性之用。本實驗中以141個經過病理驗證的病例進行測試，包含93個良性以及48個惡性的病例，比較使用本篇論文的方法所選的影像以及所得彈力最差的影像、壓力最大時的影像和醫生所選之影像，並計算Mann-Whitney U測試、效能以及ROC曲線來評估結果。經由實驗，SNRe的準確率為84.40%，敏感度為83.33%，特異性為84.95%，而ROC曲線的Az值則是0.90；CNRe則是有82.27%的準確率，79.17%敏感度，83.87%特異性，Az值為0.88，兩者均有不錯的效率，因此我們歸納出以此種方式進行影像品質的量化並挑選出最具代表性影像是可行並且較醫生挑選為客觀的。此外，為了縮減計算分析的時間，亦提出了一種fast-selection方式來挑選代表影像，此方法將只針對第一張影像進行腫瘤切割，經測試仍有一定的準確度並且大幅的減少分析時間。

Recently, the sonoelastography has been the most general technique to measure the tumor strain. In the sonoelastography, the physicians need to lightly compress a tumor to obtain a dynamic elastographic image sequence which is composed of continuous elastographic slice. A representative slice of the dynamic elastographic image sequence will be selected by the physician and the quality of this selected slice will affect the diagnosis result. The purpose of this study is to quantify the elastographic images quality and select a representative slice from an elastography movie file. This study also proposes a semi-automatic segmentation to find the tumor contour for calculating the hard ratio of tumor. Utilizing a group of seeds given by the user in the first slice, the automatic segmentation using the edge-detection and region growing methods is applied in the first slice and then the subsequent slices. Moreover, the seeds of the subsequent slices will be moved according to the tumor displacement to improve the segmentation results. After finding the tumor contours, two quality quantification methods, the signal to noise ratio of (SNRe) and contrast to noise ratio (CNRe) of elastographic slice, are computed according to the uniformity inside the selected region or the contrast of the tumor and the surrounding normal tissue. Finally, find a representative slice based on the quantification and use the selected slice to differentiate the benign and the malignant lesions. In this study, 141 biopsy-proved sonoelastography composed of 93 benign and 48 malignant masses are used to evaluate the performance of the quantification methods. In the experiments, the diagnosis results of the slices selected by two proposed methods are compared with those of the maximum compression slices, maximum strain slices, and the slices selected by physicians. The Mann-Whitney U test, performance indexes, and receiver operation curve (ROC) are applied to examine the effectiveness of the proposed quantification methods. According to the result of experiment, the accuracy, sensitivity, specificity, and the Az value for the SNRe are 84.40%, 83.33%, 84.95% and 0.90, respectively and for the CNRe are 82.27%, 79.17%, 83.87% and 0.88, respectively. We can conclude that using the quantification methods to select the representative slice of the elastography is practicable and more objective than that selected by the physician. Moreover, to reduce the run time of the quantification analysis in this paper, a smart fast-selection method is also proposed and only the tumor contour of the selected slice is required to be segmented. The fast-selection method can achieve an acceptable performance and greatly reduce the execution time of the analysis.