由於人工智慧(Artificial Intelligence, AI)和深度學習(Deep Learning, DL)的興起，許多應用卷積神經網路(Convolutional Neural Network, CNN)於醫學影像分割的研究日益受到重視。但是，其中仍存一些問題待解決，如：在分割器官或病變目標的位置、形狀、尺度變化很大及邊界模糊。這也表示，基於卷積神經網路的影像分割方法，其上下文特徵訊息提取能力不足的問題仍有待改進。在本論文中，我們在卷積神經網路架構之上，提出兩個網路模塊。首先，在編碼器端(Encoder)和解碼器端(Decoder)之間引入了多個全局特徵空間注意力引導模塊(Global Feature Spatial Attention Guidance Module, GFSAG Module)，旨在藉著跳躍連接，為解碼器提供不同階段(Stage)的全局上下文特徵訊息。更進一步地，我們提出了一個尺度感知Inception融合模塊(Scale-Aware Inception Fusion Module, SIF Module)來融合不同階段的上下文特徵訊息。此外，我們在解碼器端引入深度監督(Deep Supervision)機制，在上取樣時使用輔助損失函數，以提高分割性能。在實驗的部分，我們採用兩個醫學影像分割的資料集：(1) 處於危險狀態的胸部器官CT (Computed Tomography)影像分割資料集(Segmentation of Thoracic Organs at Risk in CT Images Dataset, SegTHOR Dataset)，和(2) 肝臟腫瘤分割資料集(Liver Tumor Segmentation Dataset, LiTS Dataset)。實驗結果顯示本研究所提出的方法優於其他分割方法的效能。

Due to the rise of Artificial Intelligence (AI) and Deep Learning (DL), a lot of research concerning medical image segmentation based on the application of Convolutional Neural Network (CNN) has attracted more and more attention. However, there are still some problems to be solved, such as large changes in the position, shape, scale, and blurred boundaries of the segmented organ or lesion target. This also means that the image segmentation methods based on CNN have insufficient ability to extract contextual feature information, and thus they still need to be improved. In this thesis, we propose two network modules based on CNN architecture. First, Global Feature Spatial Attention Guidance (GFSAG) Module is introduced between the Encoder end and the Decoder end, aiming to provide Decoder with global context feature information of different stages by using skip connection. Furthermore, we propose a Scale-Aware Inception Fusion Module (SIF Module) to fuse contextual feature information of different scales. In addition, we introduce a Deep Supervision mechanism at the Decoder end, and use an auxiliary loss function during upsampling to improve segmentation performance. In the experiment, we adopt two medical image segmentation data sets: (1) Segmentation of Thoracic Organs at Risk in CT (Computed Tomography) Images Dataset (SegTHOR Dataset), and (2) Liver Tumor Segmentation Dataset (LiTS Dataset). The experimental results show that the method proposed in this study performs better than other segmentation methods do.

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