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研究生:王詣賢
研究生(外文):Yi-HsienWang
論文名稱:利用深度學習對多對比MRI影像進行腦腫瘤影像分割
論文名稱(外文):Brain Tumor Segmentation Using Deep learning from Multi-Contrast MRI
指導教授:吳明龍
指導教授(外文):Ming-Long Wu
學位類別:碩士
校院名稱:國立成功大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2019
畢業學年度:107
語文別:英文
論文頁數:50
中文關鍵詞:腦腫瘤影像分割深度學習全卷積網絡U-net核磁共振影像
外文關鍵詞:Brain TumorSegmentationDeep LearningFCNU-netMagnetic Resonance Imaging
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隨著深度學習在機器視覺領域上的巨大成功,對於深度學習在醫學影像方面的應用也越來越受到重視,藉由分割方法來偵測腦腫瘤是非常重要的,因為病患的存活率十分依賴偵測的準確性以及臨床診斷的花費時間,神經膠質瘤(Gliomas)是最常見的腦腫瘤並且擁有不規則形狀以及模糊邊界的特性,使之被歸類為難以偵測的腫瘤的其中一種。我們將展示針對多對比核磁共振影像在腦腫瘤影像分割上的一個深度學習全自動模型,這個方法是基於全卷積網絡的原理所建構的並且以U-net結構為模型。我們使用了MICCAI (Medical Image Computing and Computer Assisted Intervention)舉辦的腦腫瘤分割挑戰所提供的資料庫做為資料來源,並且將我們的模型使用在BraTS 2018的比賽所提供的驗證資料集上,結果在完整腫瘤上達到了平均Dice相似係數 0.87、在核心腫瘤上達到 0.76、在對比增強腫瘤上達到 0.71,並且在中位數的表現上分別是 0.90、0.84、0.80。
With the huge success of deep learning in the field of computer vision, there is rising awareness of its application in medical image. Detection of brain tumor using a segmentation approach is critical in cases, where survival of a subject depends on an accurate and timely clinical diagnosis. Gliomas are the most commonly found tumors having irregular shape and ambiguous boundaries, making them one of the hardest tumors to detect. We present a fully automatic deep learning approach for brain tumor segmentation in multi-contrast magnetic resonance image. The proposed approach is based on fully convolutional network (FCN) and using U-net as the model. Using the dataset provided for the Brain Tumor Segmentation (BraTS) Challenge by the Medical Image Computing and Computer Assisted Intervention (MICCAI) society. Our proposal was validated in the BraTS2018 leaderboard dataset and achieve mean Dice Similarity Coefficient metric of 0.87 in the full tumor region, 0.76 in the tumor core region and 0.71 in the enhancing tumor region, also median Dice Similarity Coefficient metric of 0.90, 0.84, 0.80 for the full tumor, tumor core, and enhancing tumor, respectively.
中文摘要 I
Abstract II
誌謝 III
Contents IV
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 MRI Contrasts 2
1.3 CNN and FCN 4
Chapter 2 Related Work 8
Chapter 3 Method 13
3.1 Pre-processing 15
3.2 Proposed Network Architecture 16
3.3 Post-Processing 21
Chapter 4 Experiment Setup 22
4.1 Database 22
4.2 Setup 23
4.3 Evaluation 23
Chapter 5 Results and Discussion 25
5.1 Hyperparameters 25
5.2 Comparison and BraTS2015 Result 32
5.3 BraTS2018 Competition Data Result 40
5.4 Comparison of A Single U-net with Our Method 46
Chapter 6 Conclusion and Future Work 48
Chapter 7 References 49
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