TY - GEN
T1 - Deep Joint Segmentation of Liver and Cancerous Nodules from Ct Images
AU - Elmenabawy, Nermeen A.
AU - Elnakib, Ahmed
AU - Moustafa, Hossam El Din
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/9/8
Y1 - 2020/9/8
N2 - A framework is proposed for joint liver and cancerous nodule segmentation from abdomen computed tomography (CT) images. The proposed framework consists of three main units. First, a preprocessing unit is used to enhance the image contrast. Second, two different deep convolutional-deconvolutional neural networks (CDNN), namely, Alexnet and Resnet18 models, are investigated to extract the features of liver images. Finally, a pixel wise classification unit is performed to provide the final segmentation maps of the liver and tumors. Results on the challenging MICCAI'2017 liver tumor segmentation (LITS) database, using Alexnet model and 4-fold cross-validation, achieve a Dice similarity coefficient of 90.4% for liver segmentation and of 62.4% for lesion segmentation. Comparative results with related techniques for joint liver and tumor segmentations show the effectiveness of the proposed framework.
AB - A framework is proposed for joint liver and cancerous nodule segmentation from abdomen computed tomography (CT) images. The proposed framework consists of three main units. First, a preprocessing unit is used to enhance the image contrast. Second, two different deep convolutional-deconvolutional neural networks (CDNN), namely, Alexnet and Resnet18 models, are investigated to extract the features of liver images. Finally, a pixel wise classification unit is performed to provide the final segmentation maps of the liver and tumors. Results on the challenging MICCAI'2017 liver tumor segmentation (LITS) database, using Alexnet model and 4-fold cross-validation, achieve a Dice similarity coefficient of 90.4% for liver segmentation and of 62.4% for lesion segmentation. Comparative results with related techniques for joint liver and tumor segmentations show the effectiveness of the proposed framework.
UR - http://www.scopus.com/inward/record.url?scp=85096216586&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85096216586&partnerID=8YFLogxK
U2 - 10.1109/NRSC49500.2020.9235097
DO - 10.1109/NRSC49500.2020.9235097
M3 - Conference contribution
AN - SCOPUS:85096216586
T3 - National Radio Science Conference, NRSC, Proceedings
SP - 296
EP - 301
BT - Proceedings of 2020 37th National Radio Science Conference, NRSC 2020
A2 - Sadek, Rowayda
A2 - Ashour, Mohamed
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 37th National Radio Science Conference, NRSC 2020
Y2 - 8 September 2020 through 10 September 2020
ER -