隨著多參數核磁共振影像(mp-MRI)的發展，對於診斷前列腺癌的準確性有所提高，完整的mp-MRI影像包括T2加權影像(T2W)、擴散加權影像(DWI)、擴散係數影像(ADC)和動態對比增強影像(DCE-MRI)。
在本論文中，首先利用直方圖參數來分析DCE-MRI影像中對比劑最大洗入斜率(MWS)和延遲洗出斜率(DPS)於攝護腺腫瘤組織與正常組織的區分能力。結果顯示，在攝護腺的過度區，變異係數唯一在MWS中可以區分腫瘤與正常組織的參數，而在DPS中有較多的參數可以區分腫瘤(如 平均值、偏度、10%、25%、50%、75%和90%)。攝護腺外圍區的部分，在MWS分析中，除了峰度和偏度外，其他直方圖參數對於區分組織，都有顯著的差異。在DPS中有顯著差異的參數為標準差、四分位距、修正過半峰全寬，百分位數距(90%-10%)。在攝護腺的過度區與外圍區的MWS 和 DPS中，有不同的直方圖參數可以被用來區分腫瘤。
最後本論文使用以SegNet為基礎的深度卷積神經網路模型來自動分割攝護腺過度區、外圍區和腫瘤的區域。本文中使用PROSTATEx的數據集中的影像來訓練模型，將三種不同序列的核磁共振影像，分別為T2W、DWI和ADC等三種影像，並將其結合為一張三通道(RGB)的影像，並在影像上圈選出三個區域的範圍，最後利用上述三種影像做不同排列的分組來放入模型訓練。結果發現，在T2W + DWI + ADC影像組合表現出較好的分割效能，而在個別區域分割的相似係數分別為過度區(90.45%)、外圍區(70.04%)和腫瘤區(52.73%)，使用深度卷積神經網路模型來診斷腫瘤以及分割區域是相當有潛力的。
本文的結論為，對於DCE-MRI 時間強度曲線進行直方圖分析，可以用來區分 過度區與外圍區中的良性前列腺組織和腫瘤，並且深度卷積神經模型於影像序列分析中，用來輔助診斷腫瘤有相當大的潛力。

The accuracy in diagnosing prostate cancer (PCa) has increased with the development of multiparametric magnetic resonance imaging (mp-MRI). A complete mp-MRI contains T2-weighted (T2W) images, diffusion-weighted images (DWIs), the corresponding apparent diffusion coefficient (ADC) map and dynamic contrast
-enhanced MRI (DCE-MRI). However, the diagnostic accuracy and efficiency of mp-MRI still have room for improvement. In this thesis, we aimed to establish new perfusion variables to analyze histograms and use the DCNN model to segment PCa and prostate zones in mp-MRI. Further improvement of diagnostic performance in prostate MRI.
In this thesis, we evaluated the performance of histogram analysis in the time course of DCE-MRI for differentiating cancerous tissues from benign tissues in the prostate. The results showed that the coefficient of variation (CV) was the only significantly different parameter of the maximum difference wash-in slope (MWS)), whereas many parameters of the delay phase slope (DPS) (mean, skewness, P10, P25, P50, P75 and P90) differed significantly in the transitional zone (TZ) of prostate. In the peripheral zone (PZ), all parameters of the MWS exhibited significant differences, except kurtosis and skewness. The standard deviation (SD), interquartile range (IQR), modified full width at half-maximum (mFWHM), percentile (P90P10) and Range were also significant differences in the DPS. Different histogram parameters of the MWS and DPS should be applied in the TZ and PZ.
We presented a method for autosegmenting the prostate zone and cancer region by using SegNet, a deep convolution neural network (DCNN) model. We used PROSTATEx dataset to train the model and combined different sequences into three channels of a single image. For each subject, all slices that contained the TZ, PZ and PCa region were selected. The datasets were produced using different combinations of images, including T2W images, DWI and apparent diffusion coefficient (ADC) images. Among these groups, the T2W + DWI + ADC images exhibited the best performance with a dice similarity coefficient of 90.45% for the TZ, 70.04% for the PZ, and 52.73% for the PCa region.
In conclusion, we conducted a histogram analysis of DCE-MRI time course data to distinguish between benign prostate tissues and tumors in the TZ and peripheral zone (PZ). Image sequence analysis with a DCNN model has the potential to assist PCa diagnosis.

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