Parameterized Gompertz-Guided Morphological AutoEncoder for Predicting Pulmonary Nodule Growth

• Published in: IEEE transactions on medical imaging Vol. 42; no. 12; pp. 3602 - 3613
• Main Authors: Fang, Jiansheng, Wang, Jingwen, Li, Anwei, Yan, Yuguang, Liu, Hongbo, Li, Jiajian, Yang, Huifang, Hou, Yonghe, Yang, Xuening, Yang, Ming, Liu, Jiang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.12.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: autoencoder; Computed tomography; Datasets; Decoders; Diagnosis; Gompertz curves; Gompertz function; Growth kinetics; growth prediction; Growth rate; Humans; Lung; Lung cancer; Lung Neoplasms - diagnostic imaging; Lung Neoplasms - pathology; Lung nodules; Mathematical models; Medical imaging; Morphology; Nodules; Parameterization; Predictive models; Progressions; Pulmonary nodule; Radiographic Image Interpretation, Computer-Assisted - methods; Solitary Pulmonary Nodule - diagnostic imaging; Task analysis; Tomography, X-Ray Computed - methods; Tumors; Visualization

The growth rate of pulmonary nodules is a critical clue to the cancerous diagnosis. It is essential to monitor their dynamic progressions during pulmonary nodule management. To facilitate the prosperity of research on nodule growth prediction, we organized and published a temporal dataset called NLS...