SAtUNet: Series atrous convolution enhanced U‐Net for lung nodule segmentation

Precise and unambiguous segmentation of pulmonary nodules from the CT images is imperative for a CAD framework implementation delineated for the prognosis of lung cancer. Lung nodule segmentation is an appealing research discipline for accurate dismemberment of lung cancer but the irregularity in sh...

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Vydáno v:International journal of imaging systems and technology Ročník 34; číslo 1
Hlavní autoři: Selvadass, Salomi, Bruntha, P. Malin, Sagayam, K. Martin, Günerhan, Hatıra
Médium: Journal Article
Jazyk:angličtina
Vydáno: Hoboken, USA John Wiley & Sons, Inc 01.01.2024
Wiley Subscription Services, Inc
Témata:
Computed tomography
computer‐aided detection
Convolution
dilated convolution
Feature maps
lung nodule segmentation
Nodules
Shades
U‐Net
ISSN:0899-9457, 1098-1098
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Shrnutí:Precise and unambiguous segmentation of pulmonary nodules from the CT images is imperative for a CAD framework implementation delineated for the prognosis of lung cancer. Lung nodule segmentation is an appealing research discipline for accurate dismemberment of lung cancer but the irregularity in shades, contours, and compositions, and the affinity between the tumors and the neighboring regions makes it an arduous task. This paper proffers a series atrous convolution enhanced U‐Net which uses a series of concatenated dilated convolution blocks after every stage in the encoder and decoder path. Our approach helps in obtaining the quintessential components from the feature maps, in addition to the absolute convergence of the model. It is largely assessed on the publicly accessible LIDC‐IDRI dataset. The average Dice Similarity Coefficient (DSC) obtained is 81.10% with an Intersection over Union (IoU/ Jaccard Index) of 72.24%. Exploratory outcomes prove that our architecture achieves ameliorate performance.
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ISSN:0899-9457
1098-1098
DOI:10.1002/ima.22964