Computer-assisted framework for machine-learning-based delineation of GTV regions on datasets of planning CT and PET/CT images.
We have proposed a computer-assisted framework for machine-learning-based delineation of gross tumor volumes (GTVs) following an optimum contour selection (OCS) method. The key idea of the proposed framework was to feed image features around GTV contours (determined based on the knowledge of radiation oncologists) into a machine-learning classifier during the training step, after which the classifier produces the 'degree of GTV' for each voxel in the testing step. Initial GTV regions were extracted using a support vector machine (SVM) that learned the image features inside and outside each tumor region (determined by radiation oncologists). The leave-one-out-by-patient test was employed for training and testing the steps of the proposed framework. The final GTV regions were determined using the OCS method that can be used to select a global optimum object contour based on multiple active delineations with a LSM around the GTV. The efficacy of the proposed framework was evaluated in 14 lung cancer cases [solid: 6, ground-glass opacity (GGO): 4, mixed GGO: 4] using the 3D Dice similarity coefficient (DSC), which denotes the degree of region similarity between the GTVs contoured by radiation oncologists and those determined using the proposed framework. The proposed framework achieved an average DSC of 0.777 for 14 cases, whereas the OCS-based framework produced an average DSC of 0.507. The average DSCs for GGO and mixed GGO were 0.763 and 0.701, respectively, obtained by the proposed framework. The proposed framework can be employed as a tool to assist radiation oncologists in delineating various GTV regions.
Graduate School of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-ku, Fukuoka 812-8582, Japan.
Koujiro
K
Ikushima
Faculty of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-ku, Fukuoka 812-8582, Japan arimurah@med.kyushu-u.ac.jp.
Hidetaka
H
Arimura
Graduate School of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-ku, Fukuoka 812-8582, Japan.|Research Fellow of the Japan Society for the Promotion of Science.
Ze
Z
Jin
Faculty of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-ku, Fukuoka 812-8582, Japan.
Hidetake
H
Yabu-Uchi
Saiseikai Fukuoka General Hospital, 1-3-46, Tenjin, Chuo-ku, Fukuoka 810-0001, Japan.
Jumpei
J
Kuwazuru
Saga Heavy Ion Medical Accelerator in Tosu, 415, Harakoga-cho, Tosu 841-0071, Japan.
Yoshiyuki
Y
Shioyama
Faculty of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-ku, Fukuoka 812-8582, Japan.
Tomonari
T
Sasaki
Faculty of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-ku, Fukuoka 812-8582, Japan.
Hiroshi
H
Honda
Faculty of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-ku, Fukuoka 812-8582, Japan.
Masayuki
M
Sasaki
D000368:Aged; D000369:Aged, 80 and over; D005260:Female; D006801:Humans; D000069550:Machine Learning; D008297:Male; D000072078:Positron Emission Tomography Computed Tomography; D011857:Radiographic Image Interpretation, Computer-Assisted; D060388:Support Vector Machine; D047368:Tumor Burden

18F-fluorodeoxyglucose (FDG)-positron emission tomography (PET); gross tumor volume (GTV); image segmentation; machine learning; planning computed tomography