肺栓塞（Pulmonary Embolism,PE）是人類與癌癥相關(guān)的死亡的最常見原因之一。用于醫(yī)學(xué)疾病篩查的計(jì)算機(jī)斷層掃描（Computer Tomography,CT）是肺栓塞敏感性高且早期發(fā)現(xiàn)的無創(chuàng)診斷方法,可大大提高生存率。但是,解釋醫(yī)學(xué)圖像并制定評(píng)估或護(hù)理決策需要專門合格的醫(yī)學(xué)專家,當(dāng)前解釋診斷圖像的方法是費(fèi)力、費(fèi)時(shí)、昂貴且容易出錯(cuò)的。因此,基于神經(jīng)網(wǎng)絡(luò)模型的輔助診斷具有重要意義,該模型將自動(dòng)提供診斷建議。深度學(xué)習(xí)的最新發(fā)展鼓勵(lì)我們重新考慮臨床診斷專注于醫(yī)學(xué)圖像的方式。事實(shí)證明,早期發(fā)現(xiàn)對(duì)于為患者提供最大的康復(fù)和生存可能性至關(guān)重要。在本文中,我們提出了一種基于CT圖像的神經(jīng)網(wǎng)絡(luò)框架,對(duì)肺栓塞和結(jié)節(jié)進(jìn)行了全自動(dòng)分割和分類。我們的工作包括兩部分:PE分割（預(yù)處理和訓(xùn)練模型以進(jìn)行PE分割）和分類（將候選結(jié)節(jié)診斷和分類為良性或惡性）。對(duì)于PE分割,我們結(jié)合CT窗口技術(shù)和圖像裁剪,設(shè)計(jì)了一種新的有效圖像預(yù)處理方法。建立的該模型是編碼器-解碼器卷積網(wǎng)絡(luò),剩余塊代替原始卷積塊用于U-Net。為了進(jìn)行分類,奇異結(jié)節(jié),分別構(gòu)建了兩個(gè)深層3D Conv Nets用于結(jié)節(jié)檢測和分類。此外,我們驗(yàn)證... 

【文章來源】：西南科技大學(xué)四川省

【文章頁數(shù)】：63 頁

【學(xué)位級(jí)別】：碩士

【文章目錄】：
摘要（Chinese Abstract）
ABSTRACT 英文摘要
1.Introduction
    1.1 Overview
    1.2 Dissertation Outline and Contribution
2.Literature Review
    2.1 Background
    2.2 Overview
    2.3 Popular Algorithms
        2.3.1 Supervised Machine Learning Algorithms
        2.3.2 Unsupervised Machine Learning Algorithms
        2.3.3 Semi-supervised Machine Learning Algorithms
        2.3.4 Reinforcement Machine Learning Algorithms
        2.3.5 Recommender Systems
        2.3.6 Deep Learning
3.Segmentation of Pulmonary Embolism
    3.1 Segmentation of Pulmonary Embolism using Neural Networks
    3.2 Related Works
    3.3 Methodology
        3.3.1 Dataset
        3.3.2 Data Preparation
        3.3.3 Image Cropping
        3.3.4 Window Technique
        3.3.5 Standard Normalization
        3.3.6 Image augmentation
        3.3.7 Image Post-Processing
        3.3.8 Evaluation
    3.4 Experiments
        3.4.1 System Specification and tools
    3.5 Results and Discussion
4.Classification of Pulmonary Nodules
    4.1 Classification of Pulmonary Nodules using Neural Networks
    4.2 Related Work
    4.3 Methodology
        4.3.1 Neural Network Framework for Nodule Detection
        4.3.2 3D Faster R-CNN with Deep3D Dual Path Net for Nodule Detection
        4.3.3 Gradient Boosting Machine3D Dual Path Net Function for Nodule Classification
        4.3.4 Neural Network for Fully Automated PE CT Nodules Diagnosis
    4.4 Experiments
        4.4.1 Datasets
        4.4.2 System Specification and tools
        4.4.3 Preprocessing
    4.5 Results
        4.5.1 Neural network for Nodule Detection
        4.5.2 Neural network for Nodule Classification
        4.5.3 Compared to experienced physicians on their individual positive nodules
    4.6 Discussion
        4.6.1 Nodule Detection
        4.6.2 Classification of Nodules
5.Conclusion and Future work
    5.1 Conclusion
    5.2 Future Work
Acknowledgements
References
Achievements
Research achievements during the Undergraduate degree



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