Deep3DSCan: Deep residual network and morphological descriptor based framework forlung cancer classification and 3D segmentation
Open Access
- 23 April 2020
- journal article
- research article
- Published by Institution of Engineering and Technology (IET) in IET Image Processing
- Vol. 14 (7), 1240-1247
- https://doi.org/10.1049/iet-ipr.2019.1164
Abstract
With the increasing incidence rate of lung cancer patients, early diagnosis could help in reducing the mortality rate. However, accurate recognition of cancerous lesions is immensely challenging owing to factors such as low contrast variation, heterogeneity and visual similarity between benign and malignant nodules. Deep learning techniques have been very effective in performing natural image segmentation with robustness to previously unseen situations, reasonable scale invariance and the ability to detect even minute differences. However, they usually fail to learn domain-specific features due to the limited amount of available data and domain agnostic nature of these techniques. This work presents an ensemble framework Deep3DSCan for lung cancer segmentation and classification. The deep 3D segmentation network generates the 3D volume of interest from computed tomography scans of patients. The deep features and handcrafted descriptors are extracted using a fine-tuned residual network and morphological techniques, respectively. Finally, the fused features are used for cancer classification. The experiments were conducted on the publicly available LUNA16 dataset. For the segmentation, the authors achieved an accuracy of 0.927, significant improvement over the template matching technique, which had achieved an accuracy of 0.927. For the detection, previous state-of-the-art is 0.866, while ours is 0.883.Keywords
This publication has 34 references indexed in Scilit:
- Computer-Aided Diagnosis Systems for Lung Cancer: Challenges and MethodologiesInternational Journal of Biomedical Imaging, 2013
- Screening and early—detection of lung cancerAnnals of Oncology, 2012
- Ensemble sparse classification of Alzheimer's diseaseNeuroImage, 2012
- Stereotactic Body Radiation Therapy for Inoperable Early Stage Lung CancerJAMA, 2010
- Methodology for automatic detection of lung nodules in computerized tomography imagesComputer Methods and Programs in Biomedicine, 2009
- Automated lung segmentation for thoracic CT: Impact on computer-aided diagnosis1Academic Radiology, 2004
- Computerized scheme for automated detection of lung nodules in low-dose computed tomography images for lung cancer screening1Academic Radiology, 2004
- Automated lung segmentation in X-ray computed tomography: development and evaluation of a heuristic threshold-based scheme1Academic Radiology, 2003
- Automated detection of pulmonary nodules in helical CT images based on an improved template-matching techniqueIEEE Transactions on Medical Imaging, 2001
- A Meta-Analysis of Thoracic Radiotherapy for Small-Cell Lung CancerThe New England Journal of Medicine, 1992