近年來由於國人晚婚之影響，女性卵巢生殖功能的異常與老化，在不孕症中佔有相當的關連性；卵巢內卵泡個數與成長狀況對於多囊性卵巢症候群與人工生殖資訊的評估，為極重要的根據。過去臨床診斷上大都應用2D超音波，提供臨床上卵巢內卵泡的剖面影像；陰道超音波（Intravaginal ultrasound）之發展，更進一步提供三維影像資訊。然而使用該影像系統，雖然能以手動圈選方式，進行對卵泡分析二維影像資訊與單一卵泡三維重建，但是，欲瞭解卵巢內所有卵泡資訊，必須逐一分析，花費大量時間與無法準確的提供資訊。故本研究的目的之一為發展自動化圈選取代手動描繪的邊界搜尋方法，並且建立多重三維影像，期望能提供臨床專業人員更快速且客觀的搜尋出卵巢中卵泡影像，提供卵泡幾何結構上的相關參數之定量描述與分佈狀況。
研究中我們利用自行研發的卵泡量化評估系統，實際對真實卵泡影像，進行影像重建與參數評估。為了驗證系統的重建方法，我們則利用卵泡假體的體積重建，以驗證重建方法的準確性；驗證結果精確率可達90 % 以上。本研究所提出的搜索法確實能取代手繪邊界的描繪方法；而多重三維影像的重建，更能清楚的描述卵巢內卵泡個數、實際大小與空間位置，提供醫師在診斷卵巢病變上更多的參考。

Owing to the influence of the later marriage in recent years, the occurrence of Infertility is increasing on account to the aging of female ovarian function. Follicular number and growth are important factors for estimating Polycystic Ovary Syndrome (PCOS) and Assisted Reproductive Techniques (ART). In the past, two-dimensional ultrasound system has popularly been applied in clinical diagnosis to acquire the cross-sectional image of ovarian follicles. The development of Intravaginal ultrasound can also provide the information of three-dimension image further. Although we can utilize the imaging system to analyze 2D image information and reconstruct 3D model for single follicle, it is time-consuming and only obtains inaccurate information to analyze all follicles in the ovary. Thus, the first objective of this study was to develop an automatic boundary search method for replacing the manual one and further build multiple 3D models. Hopefully the objective measurement tool enables the clinicians in searching follicular boundary of ovary and provides more accurate information about follicles.
In the present study, we reconstructed the ultrasonic images and assessed the parameters of true follicles with the quantitative assessment system. In order to verify the accuracy of the systematic reconstruction method, we constructed a phantom model to imitate the ovarian follicles. The final results showed that our implementation could elevate the accuracy rate up to 90%. Thus, our results demonstrate that the proposed automatic algorithm was more efficient in follicular boundary detection compared to the manual one. The multiple reconstructed 3-D model could delineate the number, size and location of ovarian follicles clearly which can provide clinicians alternative tools and useful information for comprehend important ovarian diseases like polycystic ovary syndrome.

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