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Cascaded Dilated Deep Residual Network for Volumetric Liver Segmentation From CT Image
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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: 2023
Pages: 13
Source title:
Research Anthology on Improving Medical Imaging Techniques for Analysis and Intervention
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-6684-7544-7.ch059
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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.
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