3D Cell Segmentation by Spatial Clustering of Subcellular Organelles

• Main Authors: Cheng-Lin Tsai, 蔡政霖
• Other Authors: Jyh-Ying Peng
• Format: Others
• Language: en_US
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/81778083199883054045

碩士 === 國立陽明大學 === 生物醫學資訊研究所 === 102 === Automatic segmentation of cell images is an essential task in a variety of biomedical applications. There are six main classes of approaches: intensity thresholding, feature detection, morphological ?ltering, region accumulation, deformable model ?tting, and other approaches. In this thesis, we investigate whether spatial clustering of subcellular organelles is useful for 3D cell segmentation. We used CHO cell 3D images as our dataset. The nuclear channel is segmented by double Otsu methods and mitochondrial channel is segmented by adaptive local thresholding. We calculated the spatial centroid and weighted centroid of the mitochondria and nuclei, and then used unsupervised clustering to group the mitochondria. We used the spatial extent of mitochondria in the same group as individual cell regions. Because there are several unsupervised clustering methods, we hope to know which method yields higher accuracy for cell segmentation. We compared the performance of GMM clustering, K-means, hierarchical clustering and normalized cuts methods. Regions of interest (ROI) for each cell in the 3D images are manually labeled slice-by-slice, and used as the gold standard for accuracy calculation. The following are results using methods that include nucleus centroids as data point. K-means clustering (81.43%) and GMM clustering (81.75%) with nucleus centroids initialization have higher accuracy than hierarchical clustering with average linkage (77.18%). We compared K-means with (81.22%) or without (81.43%) using nuclei centroids as initial cluster centers, and their accuracies are similar. Hierarchical clustering with nucleus centroids as data points with average (77.18%) or complete (77.02%) linkage has the same performance. Overall, K-means and GMM clustering in round and short cells have better accuracy than flat cells. GMM clustering with nucleus centroids as data points has the highest accuracy of 81.75%. GMM clustering is not suitable for whole field images, because there are many mitochondria from cells truncated by the image boundary, resulting in more mitochondrial clusters than nuclei. We designed a graphical user interface (GUI) system for K-means clustering without using nuclei centroids as initial cluster centers. The GUI was tested on another whole field 3D confocal image with manual cell ROI and achieved accuracy of 66.71%. Users can import a large number of image files for cell segmentation in our GUI. The proposed method can be applied to cell images with different subcellular organelle labels for automatic cell segmentation.