Fan-Ya Lin1, Yeun-Chung Chang2, Hsuan-Yu Huang3, Chia-Chen Li1, Yi-Chang Chen1,4, Chung-Ming Chen5. 1. Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, No. 1, Sec. 1, Jen-Ai Road, Taipei, 100, Taiwan. 2. Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan. 3. IOIP Taiwan CO., Ltd, Taipei, Taiwan. 4. Department of Medical Imaging, Cardinal Tien Hospital, New Taipei City, Taiwan. 5. Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, No. 1, Sec. 1, Jen-Ai Road, Taipei, 100, Taiwan. chung@ntu.edu.tw.
Abstract
OBJECTIVES: To propose and evaluate a set of radiomic features, called morphological dynamics features, for pulmonary nodule detection, which were rooted in the dynamic patterns of morphological variation and needless precise lesion segmentation. MATERIALS AND METHODS: Two datasets were involved, namely, university hospital (UH) and LIDC datasets, comprising 72 CT scans (360 nodules) and 888 CT scans (2230 nodules), respectively. Each nodule was annotated by multiple radiologists. Denoted the category of nodules identified by at least k radiologists as ALk. A nodule detection algorithm, called CAD-MD algorithm, was proposed based on the morphological dynamics radiomic features, characterizing a lesion by ten sets of the same features with different values extracted from ten different thresholding results. Each nodule candidate was classified by a two-level classifier, including ten decision trees and a random forest, respectively. The CAD-MD algorithm was compared with a deep learning approach, the N-Net, using the UH dataset. RESULTS: On the AL1 and AL2 of the UH dataset, the AUC of the AFROC curves were 0.777 and 0.851 for the CAD-MD algorithm and 0.478 and 0.472 for the N-Net, respectively. The CAD-MD algorithm achieved the sensitivities of 84.4% and 91.4% with 2.98 and 3.69 FPs/scan and the N-Net 74.4% and 80.7% with 3.90 and 4.49 FPs/scan, respectively. On the LIDC dataset, the CAD-MD algorithm attained the sensitivities of 87.6%, 89.2%, 92.2%, and 95.0% with 4 FPs/scan for AL1-AL4, respectively. CONCLUSION: The morphological dynamics radiomic features might serve as an effective set of radiomic features for lung nodule detection. KEY POINTS: • Texture features varied with such CT system settings as reconstruction kernels of CT images, CT scanner models, and parameter settings, and so on. • Shape and first-order statistics were shown to be the most robust features against variation in CT imaging parameters. • The morphological dynamics radiomic features, which mainly characterized the dynamic patterns of morphological variation, were shown to be effective for lung nodule detection.
OBJECTIVES: To propose and evaluate a set of radiomic features, called morphological dynamics features, for pulmonary nodule detection, which were rooted in the dynamic patterns of morphological variation and needless precise lesion segmentation. MATERIALS AND METHODS: Two datasets were involved, namely, university hospital (UH) and LIDC datasets, comprising 72 CT scans (360 nodules) and 888 CT scans (2230 nodules), respectively. Each nodule was annotated by multiple radiologists. Denoted the category of nodules identified by at least k radiologists as ALk. A nodule detection algorithm, called CAD-MD algorithm, was proposed based on the morphological dynamics radiomic features, characterizing a lesion by ten sets of the same features with different values extracted from ten different thresholding results. Each nodule candidate was classified by a two-level classifier, including ten decision trees and a random forest, respectively. The CAD-MD algorithm was compared with a deep learning approach, the N-Net, using the UH dataset. RESULTS: On the AL1 and AL2 of the UH dataset, the AUC of the AFROC curves were 0.777 and 0.851 for the CAD-MD algorithm and 0.478 and 0.472 for the N-Net, respectively. The CAD-MD algorithm achieved the sensitivities of 84.4% and 91.4% with 2.98 and 3.69 FPs/scan and the N-Net 74.4% and 80.7% with 3.90 and 4.49 FPs/scan, respectively. On the LIDC dataset, the CAD-MD algorithm attained the sensitivities of 87.6%, 89.2%, 92.2%, and 95.0% with 4 FPs/scan for AL1-AL4, respectively. CONCLUSION: The morphological dynamics radiomic features might serve as an effective set of radiomic features for lung nodule detection. KEY POINTS: • Texture features varied with such CT system settings as reconstruction kernels of CT images, CT scanner models, and parameter settings, and so on. • Shape and first-order statistics were shown to be the most robust features against variation in CT imaging parameters. • The morphological dynamics radiomic features, which mainly characterized the dynamic patterns of morphological variation, were shown to be effective for lung nodule detection.
Authors: Igor Rafael S Valente; Paulo César Cortez; Edson Cavalcanti Neto; José Marques Soares; Victor Hugo C de Albuquerque; João Manuel R S Tavares Journal: Comput Methods Programs Biomed Date: 2015-12-02 Impact factor: 5.428
Authors: Sajid A Khan; Muhammad Nazir; Muhammad A Khan; Tanzila Saba; Kashif Javed; Amjad Rehman; Tallha Akram; Muhammad Awais Journal: Microsc Res Tech Date: 2019-04-11 Impact factor: 2.769
Authors: Colin Jacobs; Eva M van Rikxoort; Thorsten Twellmann; Ernst Th Scholten; Pim A de Jong; Jan-Martin Kuhnigk; Matthijs Oudkerk; Harry J de Koning; Mathias Prokop; Cornelia Schaefer-Prokop; Bram van Ginneken Journal: Med Image Anal Date: 2013-12-17 Impact factor: 8.545