臺灣博碩士論文加值系統

腎臟-輸尿管-膀胱（Kidney-Ureter-Bladder, KUB）影像是一種低成本、低輻射、方便的放射學檢查，而急診室的臨床醫生很容易將KUB影像作為疑似尿路結石患者的一線檢查。然而沒有經驗的臨床醫生很難正確解讀 KUB 影像。若能開發出一套基於人工智慧的電腦輔助診斷（Computer Aided Diagnosis, CAD）系統便可以有效地輔助非專業的臨床醫生快速做出正確的診斷，以便安排更進一步的治療。有鑑於此，本論文旨在設計一個KUB影像的電腦輔助診斷系統，目的要用來輔助臨床醫生對尿路結石進行準確診斷。本論文所開發的CAD系統分為兩個子系統，系統一使用 Inception-ResNetV2在預處理的 KUB 影像上訓練深度學習模型來驗證提出的影像前處理架構是否具備提升模型精度的能力；系統二則是使用ResNet混合U-net進行影像分割模型的訓練，將尿路結石的輪廓正確辨識，最後在影像資料集的來源則是高雄長庚醫院泌尿科所提供之上尿路結石患者共485張影像，而在實驗結果的分析上則使用混淆矩陣來評估模型的效能，分類模型藉由混淆矩陣計算準確率、靈敏度、特異性和F1-measure來評估；而分割模型則額外多評估三個關於遮罩的指標，IoU、MIoU及FWIoU。實驗結果顯示分類模型的準確率、靈敏度、特異性和F1-measure在驗證集上分別為0.989、1.000、0.980和0.989，在測試集上分別為0.997、1.000、0.995和0.999。此外，本論文提出的模型與現有的基於 CNN 的模型相比具有很好的競爭力，語意分割模型於測試集的整體精度為0.946，其餘評估指標如Precision、Recall、F1-score、IoU、mIoU、FWIoU分別為0.984、0.952、0.968、0.937、0.837、0.905。最後經由實驗結果與專科主治醫生的驗證，本論文提出之尿路結石檢測的電腦輔助診斷系統確實可以幫助臨床醫生做出準確診斷，減少不必要的電腦斷層掃描 (CT) 相關輻射暴露和醫療成本浪費。
關鍵詞：腎-輸尿管-膀胱影像、尿路結石、電腦輔助診斷、深度學習、分類模型、語意分割

The kidney-ureter-bladder (KUB) radiograph is a low-cost, low-radiation, and convenient radiological examination that emergency physicians often use as a first-line diagnostic tool for suspected urinary tract stone patients. However, inexperienced clinical doctors may find it difficult to correctly interpret KUB images. To address this issue, a computer-aided diagnosis (CAD) system based on artificial intelligence could effectively assist non-specialist clinicians in making accurate diagnoses, thereby facilitating further treatment. Therefore, the aim of this paper is to design a CAD system for KUB images that can assist clinical doctors in accurately diagnosing urinary tract stones. The CAD system developed in this paper consists of two subsystems. System one uses Inception-ResNetV2 to train a deep learning model on preprocessed KUB images to verify the proposed image pre-processing framework's ability to improve model accuracy. System two uses ResNet mixed with U-net to train an image segmentation model that correctly identifies the contour of urinary tract stones. The image dataset was obtained from 485 KUB images of urinary tract stone patients provided by the Kaohsiung Chang Gung Memorial Hospital Department of Urology. Confusion matrices were used to evaluate the model's performance in the experimental results analysis. The classification model evaluated the accuracy, sensitivity, specificity, and F1-measure using the confusion matrix. The segmentation model also evaluated three additional mask-related indicators, IoU, MIoU, and FWIoU. The experimental results show that the classification model's accuracy, sensitivity, specificity, and F1-measure on the validation set were 0.989, 1.000, 0.980, and 0.989, respectively, and on the testing set were 0.997, 1.000, 0.995, and 0.999, respectively. In addition, the proposed model showed good competitiveness compared to existing CNN-based models. The semantic segmentation model's overall accuracy on the testing set was 0.946, and other evaluation indicators such as Precision, Recall, F1-score, IoU, mIoU, and FWIoU were 0.984, 0.952, 0.968, 0.937, 0.837, and 0.905, respectively. Finally, through the experimental results and verification by specialist doctors, the proposed computer-aided diagnosis system for urinary tract stone detection can indeed assist clinical doctors in making accurate diagnoses, reducing unnecessary computed tomography (CT) radiation exposure and medical cost waste.

Keywords: kidney-ureter-bladder, urinary tract stones, computer-aided diagnosis, deep learning, classification model, semantic segmentation.

摘要 i
Abstract iii
致謝 v
目錄 vi
表目錄 viii
圖目錄 ix
1、 緒論 (INTRODUCTION) 1
1.1. 研究背景 (General Background Information) 1
1.2. 研究動機與目的 (Research Purpose) 1
1.3. 論文架構 (Thesis Architecture) 3
2. 文獻探討 (LITERATURE REVIEW) 4
2.1. 醫學影像 (Medical imaging) 4
2.1.1. 腹部X光 (Abdominal X-ray) 4
2.1.2. 電腦斷層掃描 (Computed tomography, CT) 5
2.1.3. 磁振照影 (Magnetic Resonance Imaging, MRI) 7
2.2. 卷積神經網路 (Convolutional Neural Networks, CNN) 8
2.2.1. 卷積層 (Convolution layer) 8
2.2.2. 池化層 (Pooling layer) 9
2.2.3. 損失函數 (Loss function) 10
2.2.4. 激活函數 (Activation) 11
2.3. 殘差網路 (Residual Network) 12
2.4. Inception-ResNetV2 14
2.5. 醫學影像語意分割模型 - U-Net (Semantic Segmentation Models for Medical Imaging) 18
3. 研究方法 (METHOD) 20
3.1. 資料集 (Dataset) 20
3.2. 影像前處理 (Image preprocessing) 22
3.2.1. 影像遮罩 (Image mask) 22
3.2.2. 限制對比度自適應直方圖均衡化 (Contrast Limited Adaptive Histogram Equalization, CLAHE) 24
3.2.3. 影像裁切 (Image cropping) 27
3.3. 資料擴增 (Data augmentation) 28
3.4. 系統架構 (System Architecture) 28
4. 研究結果 (RESULTS) 31
4.1. 實驗環境 (Lab environment) 31
4.1.1. 軟硬體資訊 (Software & Hardware information) 31
4.1.2. 資料集 (Training and testing dataset) 32
4.2. 評估指標 (Evaluation metrics) 32
4.3. 數據增強對分類模型訓練的影響 (The effect of data augmentation on classification model training) 34
4.4. 系統一 - 分類模型的測試 (The classification model’s test) 35
4.4.1. ResNet50分類實驗結果 35
4.4.2. Inception-ResNetV2分類實驗結果 38
4.5. 系統二 - 語意分割模型測試 (The segmentation model’s test) 41
5. 結論與未來展望 (CONCLUSIONS & FUTURE RESEARCH) 48
6. 參考文獻 49

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