Universität zu Köln

He, Kan, Liu, Xiaoming, Shahzad, Rahil, Reimer, Robert, Thiele, Frank, Niehoff, Julius, Wybranski, Christian, Bunck, Alexander C. C., Zhang, Huimao and Perkuhn, Michael (2021). Advanced Deep Learning Approach to Automatically Segment Malignant Tumors and Ablation Zone in the Liver With Contrast-Enhanced CT. Front. Oncol., 11. LAUSANNE: FRONTIERS MEDIA SA. ISSN 2234-943X

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Abstract

Objective: Liver cancer is one of the most commonly diagnosed cancer, and energy-based tumor ablation is a widely accepted treatment. Automatic and robust segmentation of liver tumors and ablation zones would facilitate the evaluation of treatment success. The purpose of this study was to develop and evaluate an automatic deep learning based method for (1) segmentation of liver and liver tumors in both arterial and portal venous phase for pre-treatment CT, and (2) segmentation of liver and ablation zones in both arterial and portal venous phase for after ablation treatment. Materials and Methods: 252 CT images from 63 patients undergoing liver tumor ablation at a large University Hospital were retrospectively included; each patient had pre-treatment and post-treatment multi-phase CT images. 3D voxel-wise manual segmentation of the liver, tumors and ablation region by the radiologist provided reference standard. Deep learning models for liver and lesion segmentation were initially trained on the public Liver Tumor Segmentation Challenge (LiTS) dataset to obtain base models. Then, transfer learning was applied to adapt the base models on the clinical training-set, to obtain tumor and ablation segmentation models both for arterial and portal venous phase images. For modeling, 2D residual-attention Unet (RA-Unet) was employed for liver segmentation and a multi-scale patch-based 3D RA-Unet for tumor and ablation segmentation. Results: On the independent test-set, the proposed method achieved a dice similarity coefficient (DSC) of 0.96 and 0.95 for liver segmentation on arterial and portal venous phase, respectively. For liver tumors, the model on arterial phase achieved detection sensitivity of 71%, DSC of 0.64, and on portal venous phase sensitivity of 82%, DSC of 0.73. For liver tumors > 0.5cm(3) performance improved to sensitivity 79%, DSC 0.65 on arterial phase and, sensitivity 86%, DSC 0.72 on portal venous phase. For ablation zone, the model on arterial phase achieved detection sensitivity of 90%, DSC of 0.83, and on portal venous phase sensitivity of 90%, DSC of 0.89. Conclusion: The proposed deep learning approach can provide automated segmentation of liver tumors and ablation zones on multi-phase (arterial and portal venous) and multi-time-point (before and after treatment) CT enabling quantitative evaluation of treatment success.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code He, Kan UNSPECIFIED UNSPECIFIED UNSPECIFIED Liu, Xiaoming UNSPECIFIED UNSPECIFIED UNSPECIFIED Shahzad, Rahil UNSPECIFIED UNSPECIFIED UNSPECIFIED Reimer, Robert UNSPECIFIED UNSPECIFIED UNSPECIFIED Thiele, Frank UNSPECIFIED UNSPECIFIED UNSPECIFIED Niehoff, Julius UNSPECIFIED UNSPECIFIED UNSPECIFIED Wybranski, Christian UNSPECIFIED UNSPECIFIED UNSPECIFIED Bunck, Alexander C. C. UNSPECIFIED UNSPECIFIED UNSPECIFIED Zhang, Huimao UNSPECIFIED UNSPECIFIED UNSPECIFIED Perkuhn, Michael UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-583786
DOI:	10.3389/fonc.2021.669437
Journal or Publication Title:	Front. Oncol.
Volume:	11
Date:	2021
Publisher:	FRONTIERS MEDIA SA
Place of Publication:	LAUSANNE
ISSN:	2234-943X
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language Oncology Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/58378