Impact of pixel-based machine-learning techniques on automated frameworks for delineation of gross tumor volume regions for stereotactic body radiation therapy

• Material Type: 博士論文
• Title: Impact of pixel-based machine-learning techniques on automated frameworks for delineation of gross tumor volume regions for stereotactic body radiation therapy
• Author/Editor: 河田, 康雄
• Author Heading: 河田, 康雄
• Publication Date: 2018-03-20
• Publication Date (W3CDTF): 2018-03-20
• Alternative Title: 体幹部定位放射線治療における肉眼的腫瘍体積の自動抽出法に対する機械学習の効果
• Contributor: 大喜, 雅文; 杜下, 淳次; 勝田, 仁
• Degree grantor/type: 九州大学
• Date Granted: 2018-03-20
• Date Granted (W3CDTF): 2018-03-20
• Dissertation Number: 甲第13916号
• Degree Type: 博士（保健学）
• Conferring No. (Dissertation): 甲第13916号
• Text Language Code: eng
• Target Audience: 一般
• Note (General): The aim of this study was to investigate the impact of pixel-based machine learning (ML) techniques, i.e.,fuzzy-c-means clustering method (FCM), and the artificial neural network (ANN) and support vector machine (SVM), on an automated framework for delineation of gross tumor volume (GTV) regions of lung cancer for stereotactic body radiation therapy. The morphological and metabolic features for GTV regions, which were determined based on the knowledge of radiation oncologists, were fed on a pixel-by-pixel basis into the respective FCM, ANN, and SVM ML techniques. Then, the ML techniques were incorporated into the automated delineation framework of GTVs followed by an optimum contour selection (OCS) method, which we proposed in a previous study. The three-ML-based frameworks were evaluated for 16 lung cancer cases (six solid, four ground glass opacity (GGO), six part-solid GGO) with the datasets of planning computed tomography (CT) and 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/CT images using the three-dimensional Dice similarity coefficient (DSC). DSC denotes the degree of region similarity between the GTVs contoured by radiation oncologists and those estimated using the automated framework. The FCM-based framework achieved the highest DSCs of 0.79 ± 0.06, whereas DSCs of the ANN-based and SVM-based frameworks were 0.76 ± 0.14 and 0.73 ± 0.14, respectively. The FCM-based framework provided the highest segmentation accuracy and precision without a learning process (lowest calculation cost). Therefore, the FCM-based framework can be useful for delineation of tumor regions in practical treatment planning.; PubMed番号 : 29173908
• DOI: 10.15017/1931769; https://doi.org/10.15017/1931769
• Persistent ID (NDL): info:ndljp/pid/11194488; https://dl.ndl.go.jp/pid/11194488
• Collection: 障害者向け資料
• Collection (Materials For Handicapped People:1): テキストデータ
• Collection (particular): 国立国会図書館デジタルコレクション > デジタル化資料 > 博士論文; https://dl.ndl.go.jp/collections/A00014
• Acquisition Basis: 博士論文（自動収集）
• Date Accepted (W3CDTF): 2018-12-03T16:15:19+09:00
• Date Created (W3CDTF): 2018-05-30
• Format (IMT): application/pdf
• Access Restrictions: 国立国会図書館内限定公開
• Service for the Digitized Contents Transmission Service: 図書館・個人送信対象外
• Availability of remote photoduplication service: 可
• Periodical Title (URI): http://dx.doi.org/10.15017/1931769; http://hdl.handle.net/2324/1931769
• Data Provider (Database): 国立国会図書館 : 国立国会図書館デジタルコレクション; https://dl.ndl.go.jp

Table of Contents

2023-09-05 再収集

2023-09-05 再収集

2023-09-05 再収集

2023-12-08 再収集

2023-12-08 再収集