Interstitial Cells of Cajal(ICC)包含它周邊的神經系統存在於腸胃道中，是一種可以驅動蠕動的機制。在近幾年的研究中，ICC的數量被發現與一些腸胃的疾病相關，例如便祕、糖尿病，甚至是突變。因此定量地分析ICC細胞核的數量，或是它在立體中的長度或樣貌，是相當重要的議題。傳統上醫生是拿著一張張的影像反覆觀看並定量ICC, 這樣的方式精準但花時間，而在[1]提出了＂光學清除＂的技術後，現在我們可以看清楚更厚的切片，而傳統的方式將因為更花時間而無法適用，我們希望提出一個更有效率的方式。
過去對定量細胞數量的研究較少，有一種方式是在x-y,y-z,x-z三個方向的投影圖片做定量，再找回它們在空間中的位置。然而，在我們的狀況中，相當多的ICC細胞核被投影到一張圖中，且彼此將因太靠近（或重疊）而無法被區分開。我們利用同一顆ICC在不同張中的質心會很相近的特性，提出一個＂質心分類法＂作定量。
另外一個研究主題是ICC神經長度的定量，在過去的研究中，主要有兩個方向：一個是利用三維細線化的方式追蹤，一個則是從邊界的梯度找出神經的中軸，進而追蹤它，但兩者皆並非很有效率的方法。我們提出一個改進的方法，利用同一條ICC在兩張相鄰影像中重疊的比率，作為重建三維模型的資訊，進而重建和輸出總長度。
在實驗結果中，我們得到了較有效率的執行時間，也達到了一定的準確度，希望在不久的將來，可以幫助醫生做臨床診斷。

Interstitial Cells of Cajal(ICC) is the pacemaker of motility in the gastrointestinal tract. It is found in recent years that its amount and processes are good indicators to characterize some motility disorders such as constipation and diabetes. In fact, quantitative analysis of ICC is an important topic nowadays. Traditionally, doctors quantify the amount of ICC slice by slice by human eyes, which is accurate but tedious. After optical clearing technique proposed [1], now we are able to acquire images from deeper depth in the specimens. However, additional amount of images acquired by the new technique has made the traditional method inapplicable. Therefore, a more efficient method is required.
Few attempts have been made at the amount counting of ICC. One proposed method counts ICC in projection images in x-y, x-z, and y-z planes and back projecting them to 3-D image. However, using this method encounters the difficulties in distinguishing ICCs from each other when they are all projected to a single image in our case. We present a method called “centroid classification” which adopts the property that centroids of ICCs over slices would be located within a reasonable region to quantify ICCs.
Another research topic for ICCs is the length counting of ICC. There are two main directions in this study: 1) 3-D thinning and 2) finding the medial line. We propose a new method to solve the inefficiency of the previous proposed methods based on finding the overlap ratio of the same ICC process in adjacent images, and using this information to reconstruct the 3-D structure.
In our experiment, the two methods we proposed are efficient with acceptable accuracies which would be helpful in clinical diagnosing.

[1] Y.A. Liu, Y.C. Chung, S.T. Pan, Y.C. Hou, S.J. Peng, P.J. Pasricha, and S.C. Tang, “3-D illustration of network orientations of interstitial cells of Cajal subgroups in human colon as revealed by deep-tissue imaging with optical clearing”,Am J Physiol Gastrointest Liver Physiol 302:G1099-G1110, 2012.

[2] T. Komuro, “Structure and organization of interstitial cells of Cajal in the gastrointestinal tract”, J Physiol 576: 653-658, 2006.

[3] J.D. Huizinga, N. Zarate, G. Farrugia, “Physiology, injury, and recovery of interstitial cells of Cajal: basic and clinical science”, Gastroenterology 137: 1548-1556, 2009.

[4] Pawley JB, ”Handbook of Biological Confocal Microscopy (3rd ed.)”. Berlin: Springer ,2006

[5] Khalind A. Al-Kofahi, Sharie Lasek, Donald H. Szarowski, Christopher J Pace, George Nagy, James N Turner and Badrinth Roysam, “Rapid Automated Three-Dimensional Tracing of Neurons From Confocal Image Stacks”, IEEE transactions on information technology in biomedicine, vol. 6, no. 2,June 2002.

[6] A. Dima, M. Scholz, and K. Obermayer, “Automatic Segmentation and Skeletonization of Neurons From Confocal Microscopy Images Based on the 3-D Wavelet Traform”, IEEE transactions on imageprocessing, vol.11, no. 7, July 2002.

[7] A. Dima, M. Scholz, and K. Obermayer, “Automatic 3-D-Graph Construction of Nerve Cells from Confocal Microscopy Scans”, Tech. Univ. Berlin, Germany.

[8] W. He, T. A. Hamilton, A. R. Cohen, T. J. Holmes, C. Pace, D. H. Szarowski, J. N. Turner, and Badrinath Roysam, “Automated Three-Dimensional Tracing of Neurons in Confocal and Brightfield Images”, Microsc. Microanal. 9, 296~310, 2003.

[9] S. Scmitt, J. F. Evers, C. Duch, M.Scholz, K. Obermayer, “New Methods for the Computer-Assisted 3D Reconstruction of Neurons from Confocal Image Stacks”, Tech. Univ. Berlin, Germany, 2004.
[10] G. Lin, C. S. Bjornsson, K. L. Smith, Muhammad-Amri Abdul-Karim, J. N. Turner, W. Shain, and B. Roysam, “Automated Image Analysis Methods for 3-D Quantification of the Neurovascular Unit From Multichannel Confocal Microscope Images”

[11] Emin Oztas ,”Neuronal tracing”, Neuroanatomy, 2003, Volume 2, Pages 2-5

[12] A. Cohen, B. Roysam, and J. Turner, ‘‘Automated tracing and volume measurements of neurons from 3-D confocal fluorescence microscopy data,’’ J. Microsc. 173(2), 103–114 , Feb.1994.

[13] F. Xu, P. H. Lewis, J. E. Chad, and H. V. Wheal, ‘‘Extracting generalized cylinder models of dendritic trees from 3-D image stacks,’’ in Proc. 3-D and Multidimensional Microscopy: Image Acquisition and Processing V, Abraham Katzir, Ed., Proc. SPIE 3261, 149–158, 1998.