以可變形模型為基礎的老鼠腦部磁振影像中風區域自動分割技術

Abstract

腦血管疾病是國內十大死因的第四名，臨床上，大多數患者中風類型為缺血性中風。為了診斷此疾病，臨床前實驗多半使用嚙齒動物來做研究，醫生可藉由磁振造影偵測其腦梗塞區域並進行診斷，利用醫學影像分割提取大腦區域與缺血型中風區域。然而手動分割費時費力，因此本篇論文以大鼠作為實驗動物，使用其影像來開發一自動化演算法，以利醫生進行診斷和研究。 本論文的演算法分為三部分，首先對原始腦部磁振影像進行大腦擷取，再將其分為左右腦半球，藉由重疊左右腦半球找出異常區域，將其判定為中風區域。大腦擷取的部分是採用基於無邊緣主動輪廓模型的方法，加入了窄帶區域演算法的概念，並在迭代過程中對時間步長進行調整，使計算效率提升、準確率上升。此外，因為此模型對初始位置非常敏感，本演算法以Otsu法對原始影像進行二值化後得到一初始輪廓，將其和強化過的影像用改進的模型做處理，即可得到分割結果，並將此結果當作下一張切片的初始輪廓，大大減少了迭代次數。 左右腦半球分割的部分是接續大腦擷取，得到一乾淨的腦部影像後，再來利用邊緣偵測、梯度運算找出中線上的特徵點，用形態學將其連線產生腦部中線作為分割線，進行左右腦半球的分割。 中風區域的分割同樣是基於可變形模型的方法，將缺血型腦中風區域從老鼠大腦分割出來。基於正常和病變區域強度有差異的假設，藉由計算左右腦半球重疊區域的差值求得異常區域，並將其判定為中風區域。 本篇研究使用了34隻老鼠腦部中風影像，結果顯示本研究演算法可以準確分割老鼠大腦（Dice為96.12±0.75%，Jaccard為92.52±1.4%）和左右腦半球（右腦的Dice為97.99±0.94%，右腦的Jaccard為96.06±1.79%，左腦的Dice為97.82±1.18%，左腦的Jaccard為95.73±2.21%），對中風區域的分割也有良好的表現（Dice為77.14±21.89%，Jaccard為62.79±21.91%）。

Cerebrovascular disease is the fourth domestic cause of death. Most of the stroke patients have a ischemic stroke. In order to study this disease, rodents are usually used in preclinical experiments. By magnetic resonance imaging, doctors can detect and diagnose the cerebral infarct area, and extract brain regions and ischemic stroke regions by using image segmentation tools. However, manual segmentation is time-consuming and laborious. An automated algorithm is developed in this thesis using rats as experimental animals to help doctors conduct diagnosis and investigation. The algorithm of this thesis is divided into three parts. Firstly, the brain is extracted from surrounding region (skull and non-brain tissues), and then this region is divided into left and right brain cerebral hemispheres. Finally, the abnormal area is found by overlapping the left and right brain hemispheres, which can be determined as a stroke area. The algorithm of the brain segmentation is based on the active contour model without edges, including the concept of the narrow band, and its time step is adjusted in the iterative process, so that the calculation efficiency is improved and the accuracy is increased. Moreover, because of its sensitivity of the initial contour, the Otsu method is used to binarize the original image to obtain a rough mask. Then, the mask and the enhanced-contrast image are processed by the improved model to produce segmentation outcome which is treated as the Initial Contour of the next slice, thus greatly reducing the number of iterations. After brain extraction, to segment the left and right cerebral hemispheres, a clean brain image is processed by edge detection and gradient operations to find the feature points around the midline. Then, morphology methods are used to connect the brain midline, which performs the division of the left and right brain hemispheres. The segmentation of the stroke region is also based on a deformable model that separates the ischemic brain stroke region from the rat brain. Based on the assumption of the different intensity of the normal and the lesion regions, the abnormal region can be obtained by calculating the difference between the left and right hemisphere overlapping regions, which is determined as the stroke region. In this study, we used 34 rat brain stroke images. The results showed that the algorithm can accurately segment the rat brain (Dice = 96.12 ± 0.75%, Jaccard = 92.52 ± 1.4%) and the left and right brain hemispheres (Dice of right brain = 97.99 ± 0.94%, Jaccard of the right brain = 96.06 ± 1.79%, Dice of the left brain = 97.82 ± 1.18%, Jaccard of the left brain = 95.73 ± 2.21%). The segmentation of the stroke regions also had good performance (Dice = 77.14 ± 21.89%, Jaccard = 62.79 ± 21.91%).