Deep Deconvolutional Residual Network Based Automatic Lung Nodule Segmentation
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Deep Deconvolutional Residual Network Based Automatic Lung Nodule Segmentation Ganesh Singadkar1
· Abhishek Mahajan2 · Meenakshi Thakur2 · Sanjay Talbar1
© Society for Imaging Informatics in Medicine 2020
Abstract Accurate and automatic lung nodule segmentation is of prime importance for the lung cancer analysis and its fundamental step in computer-aided diagnosis (CAD) systems. However, various types of nodule and visual similarity with its surrounding chest region make it challenging to develop lung nodule segmentation algorithm. In this paper, we proposed the Deep Deconvolutional Residual Network (DDRN) based approach for the lung nodule segmentation from the CT images. Our approach is based on two key insights. Proposed deep deconvolutional residual network trained end to end and captures the diverse variety of the nodules from the 2D set of the CT images. Summation-based long skip connection from convolutional to deconvolutional part of the network preserves the spatial information lost during the pooling operation and captures the full resolution features. The proposed method is evaluated on the publicly available Lung Image Database Consortium and Image Database Resource Initiative (LIDC/IDRI) dataset. Results indicate that our proposed method can successfully segment nodules and achieve the average Dice scores of 94.97%, and Jaccard index of 88.68%. Keywords Computer-aided diagnosis · Lung nodule segmentation · Pulmonary nodule · Juxtapleural nodule
Introduction Lung cancer is a leading cause of cancer-related deaths and the most commonly detected cancer worldwide both in men and women. According to the American Cancer Society, the 5-year survival rate of lung cancer is 17.8% which is lower than colon 65.4%, breast 90.35%, and prostate 99.6% [29]. The presence of a pulmonary nodule is the possible reason for lung cancer. If these nodules are detected at primary stage then the chances of survival can be increased from 10–15% to 60–80% [14]. The imaging modality like computer tomography(CT) is primarily used for the diagnosis
Ganesh Singadkar
[email protected] Sanjay Talbar [email protected] 1
Department of Electronics & Telecommunication Engineering, Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded, Maharashtra, India
2
Department of Radio-diagnosis, Tata Memorial Hospital, Mumbai, India
and detection of lung cancer. According to the National Lung Screening Trial (NLST), use of CT over the other radiology techniques decreases the mortality of lung cancer by 20%. But due to advancements in scanner technology, CT scanner produces a large amount of data which makes the analysis and diagnosis of lung cancer very challenging for the radiologist. Detection of the lung nodules in such huge data is very time consuming and it increases their workload. Therefore, to speed up the diagnosis and detection process and to assist the radiologist, computer-aided diagnosis(CAD)/detection(CADx) systems are proposed. Lung nodule segmentation is a fundamental step in the CAD. Recent
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