Conventional Filtering Versus U-Net Based Models for Pulmonary Nodule Segmentation in CT Images.

Journal of Medical Systems
Joana RochaAna Maria Mendonça

Abstract

Lung cancer is considered one of the deadliest diseases in the world. An early and accurate diagnosis aims to promote the detection and characterization of pulmonary nodules, which is of vital importance to increase the patients' survival rates. The mentioned characterization is done through a segmentation process, facing several challenges due to the diversity in nodular shape, size, and texture, as well as the presence of adjacent structures. This paper tackles pulmonary nodule segmentation in computed tomography scans proposing three distinct methodologies. First, a conventional approach which applies the Sliding Band Filter (SBF) to estimate the filter's support points, matching the border coordinates. The remaining approaches are Deep Learning based, using the U-Net and a novel network called SegU-Net to achieve the same goal. Their performance is compared, as this work aims to identify the most promising tool to improve nodule characterization. All methodologies used 2653 nodules from the LIDC database, achieving a Dice score of 0.663, 0.830, and 0.823 for the SBF, U-Net and SegU-Net respectively. This way, the U-Net based models yield more identical results to the ground truth reference annotated by specialists, thus bei...Continue Reading

References

Jun 8, 2010·IEEE Transactions on Medical Imaging·Pedro QuelhasAurélio Campilho
Mar 28, 2013·International Journal of Molecular Imaging·Daniel MarkelAlexander Sun
May 29, 2015·Nature·Yann LeCunGeoffrey Hinton
Dec 17, 2015·Advances in Experimental Medicine and Biology·Lindsey A TorreAhmedin Jemal
Feb 18, 2017·Radiological Physics and Technology·Bram van Ginneken

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Citations

Sep 16, 2020·Journal of Medical Systems·Enrique Garcia-CejaMichael Alexander Riegler
Aug 9, 2021·Computer Methods and Programs in Biomedicine·Jianshe ShiJianlong Huang
Aug 31, 2021·Computers in Biology and Medicine·Yu GuXiaoqi Lu

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