Deep Deconvolutional Residual Network Based Automatic Lung Nodule Segmentation
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,Radiology Nuclear Medicine and imaging,Radiological and Ultrasound Technology
Link
http://link.springer.com/content/pdf/10.1007/s10278-019-00301-4.pdf
Reference30 articles.
1. Lung Image Database Consortium - Imaging Database Resources Initiative LIDC-IDRI. http://imaging.nci.nih.gov/ncia/login.jsf
2. Messay T, Hardie RC, Rogers SK: A new computationally efficient cad system for pulmonary nodule detection in ct imagery. Med Image Anal 14 (3): 390–406, 2010
3. Kubota T, Jerebko AK, Dewan M, Salganicoff M, Krishnan A: Segmentation of pulmonary nodules of various densities with morphological approaches and convexity models. Med Image Anal 15 (1): 133–154, 2011
4. Lassen BC, Jacobs C, Kuhnigk JM, van Ginneken B, van Rikxoort EM: Robust semi-automatic segmentation of pulmonary subsolid nodules in chest computed tomography scans. Phys Med Biol 60 (3): 1307–1323, 2015
5. Messay T, Hardie RC, Tuinstra TR: Segmentation of pulmonary nodules in computed tomography using a regression neural network approach and its application to the lung image database consortium and image database resource initiative dataset. Med Image Anal 22 (1): 48–62, 2015
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