IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Cascaded Dilated Deep Residual Network for Volumetric Liver Segmentation From CT Image

Cascaded Dilated Deep Residual Network for Volumetric Liver Segmentation From CT Image
View Sample PDF
Author(s): Gajendra Kumar Mourya (North-Eastern Hill University, Shillong, India), Manashjit Gogoi (North-Eastern Hill University, Shillong, India), S. N. Talbar (Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded, India), Prasad Vilas Dutande (Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded, India) and Ujjwal Baid (Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded, India)
Copyright: 2021
Volume: 12
Issue: 1
Pages: 12
Source title: International Journal of E-Health and Medical Communications (IJEHMC)
Editor(s)-in-Chief: Joel J.P.C. Rodrigues (Federal University of Piauí (UFPI), Teresina - Pi, Brazil; Instituto de Telecomunicações, Portugal)
DOI: 10.4018/IJEHMC.2021010103

Purchase

View Cascaded Dilated Deep Residual Network for Volumetric Liver Segmentation From CT Image on the publisher's website for pricing and purchasing information.

Abstract

Volumetric liver segmentation is a prerequisite for liver transplantation and radiation therapy planning. In this paper, dilated deep residual network (DDRN) has been proposed for automatic segmentation of liver from CT images. The combination of three parallel DDRN is cascaded with fourth DDRN in order to get final result. The volumetric CT data of 40 subjects belongs to “Combined Healthy Abdominal Organ Segmentation” (CHAOS) challenge 2019 is utilized to evaluate the proposed method. Input image converted into three images using windowing ranges and fed to three DDRN. The output of three DDRN along with original image fed to the fourth DDRN as an input. The output of cascaded network is compared with the three parallel DDRN individually. Obtained results were quantitatively evaluated with various evaluation parameters. The results were submitted to online evaluation system, and achieved average dice coefficient is 0.93±0.02; average symmetric surface distance (ASSD) is 4.89±0.91. In conclusion, obtained results are prominent and consistent.

Related Content

Sadaf Batool Naqvi, Abad Ali Shah. © 2021. 13 pages.
Eftychia Ferentinou, Despoina Pappa, Chrysoula Dafogianni. © 2021. 16 pages.
Stavros K. Archondakis. © 2021. 11 pages.
Stavros Sfikas, Victoria Alikari, Freideriki-Eleni Kourti, Chrysoula Dafogianni. © 2021. 12 pages.
Prashant Johri, Vivek sen Saxena, Avneesh Kumar. © 2021. 15 pages.
Adriana Murraças, Paula Maria Vaz Martins, Carlos Daniel Cipriani Ferreira, Tiago Marques Godinho, Augusto Marques Ferreira da Silva. © 2021. 18 pages.
Gajendra Kumar Mourya, Manashjit Gogoi, S. N. Talbar, Prasad Vilas Dutande, Ujjwal Baid. © 2021. 12 pages.
Body Bottom