IRUS Total

H-DenseUNet: Hybrid densely connected UNet for liver and tumor segmentation from CT volumes

File Description SizeFormat 
1709.07330v3.pdfAccepted version3.76 MBAdobe PDFView/Open
Title: H-DenseUNet: Hybrid densely connected UNet for liver and tumor segmentation from CT volumes
Authors: Li, X
Chen, H
Qi, X
Dou, Q
Fu, C-W
Heng, P-A
Item Type: Journal Article
Abstract: Liver cancer is one of the leading causes of cancer death. To assist doctors in hepatocellular carcinoma diagnosis and treatment planning, an accurate and automatic liver and tumor segmentation method is highly demanded in the clinical practice. Recently, fully convolutional neural networks (FCNs), including 2D and 3D FCNs, serve as the back-bone in many volumetric image segmentation. However, 2D convolutions can not fully leverage the spatial information along the third dimension while 3D convolutions suffer from high computational cost and GPU memory consumption. To address these issues, we propose a novel hybrid densely connected UNet (H-DenseUNet), which consists of a 2D DenseUNet for efficiently extracting intra-slice features and a 3D counterpart for hierarchically aggregating volumetric contexts under the spirit of the auto-context algorithm for liver and tumor segmentation. We formulate the learning process of H-DenseUNet in an end-to-end manner, where the intra-slice representations and inter-slice features can be jointly optimized through a hybrid feature fusion (HFF) layer. We extensively evaluated our method on the dataset of MICCAI 2017 Liver Tumor Segmentation (LiTS) Challenge and 3DIRCADb Dataset. Our method outperformed other state-of-the-arts on the segmentation results of tumors and achieved very competitive performance for liver segmentation even with a single model.
Issue Date: 1-Dec-2018
Date of Acceptance: 1-Jun-2018
URI: http://hdl.handle.net/10044/1/64618
DOI: https://dx.doi.org/10.1109/TMI.2018.2845918
ISSN: 0278-0062
Publisher: Institute of Electrical and Electronics Engineers
Start Page: 2663
End Page: 2674
Journal / Book Title: IEEE Transactions on Medical Imaging
Volume: 37
Issue: 12
Copyright Statement: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Keywords: cs.CV
08 Information And Computing Sciences
09 Engineering
Nuclear Medicine & Medical Imaging
Publication Status: Published
Conference Place: United States
Online Publication Date: 2018-06-11
Appears in Collections:Computing
Faculty of Engineering