An Efficient DA-Net Architecture for Lung Nodule Segmentation

A typical growth of cells inside tissue is normally known as a nodular entity. Lung nodule segmentation from computed tomography (CT) images becomes crucial for early lung cancer diagnosis. An issue that pertains to the segmentation of lung nodules is homogenous modular variants. The resemblance amo...

Full description

Saved in:
Bibliographic Details
Published in:Mathematics (Basel) 2021-07, Vol.9 (13), p.1457
Main Authors: Maqsood, Muazzam, Yasmin, Sadaf, Mehmood, Irfan, Bukhari, Maryam, Kim, Mucheol
Format: Article
Language:English
Subjects:
Atrous convolutions
Cancer
Cluster analysis
Clustering
Computed tomography
Cysts
DA-Net
Feature extraction
Image processing
Image segmentation
lung nodule segmentation
Machine learning
Mathematics
Medical imaging
Morphology
Neural networks
Nodules
online diagnosis
Optimization techniques
unsupervised learning
Vector quantization
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:A typical growth of cells inside tissue is normally known as a nodular entity. Lung nodule segmentation from computed tomography (CT) images becomes crucial for early lung cancer diagnosis. An issue that pertains to the segmentation of lung nodules is homogenous modular variants. The resemblance among nodules as well as among neighboring regions is very challenging to deal with. Here, we propose an end-to-end U-Net-based segmentation framework named DA-Net for efficient lung nodule segmentation. This method extracts rich features by integrating compactly and densely linked rich convolutional blocks merged with Atrous convolutions blocks to broaden the view of filters without dropping loss and coverage data. We first extract the lung’s ROI images from the whole CT scan slices using standard image processing operations and k-means clustering. This reduces the search space of the model to only lungs where the nodules are present instead of the whole CT scan slice. The evaluation of the suggested model was performed through utilizing the LIDC-IDRI dataset. According to the results, we found that DA-Net showed good performance, achieving an 81% Dice score value and 71.6% IOU score.
ISSN:2227-7390
2227-7390
DOI:10.3390/math9131457