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Erschienen in: Journal of Digital Imaging 3/2020

05.02.2020

Deep Deconvolutional Residual Network Based Automatic Lung Nodule Segmentation

verfasst von: Ganesh Singadkar, Abhishek Mahajan, Meenakshi Thakur, Sanjay Talbar

Erschienen in: Journal of Imaging Informatics in Medicine | Ausgabe 3/2020

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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%.
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Metadaten
Titel
Deep Deconvolutional Residual Network Based Automatic Lung Nodule Segmentation
verfasst von
Ganesh Singadkar
Abhishek Mahajan
Meenakshi Thakur
Sanjay Talbar
Publikationsdatum
05.02.2020
Verlag
Springer International Publishing
Erschienen in
Journal of Imaging Informatics in Medicine / Ausgabe 3/2020
Print ISSN: 2948-2925
Elektronische ISSN: 2948-2933
DOI
https://doi.org/10.1007/s10278-019-00301-4

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