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Current Medical Imaging

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ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

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Research Article

Disease Quantification of Liver Lymphoma in CT Images without Lesion Segmentation

Author(s): Kexin Li*, Xinwang Huang, Chunxue Sun, Qiancheng Xie and Shijie Cong

Volume 20, 2024

Published on: 09 June, 2023

Article ID: e310523217506 Pages: 10

DOI: 10.2174/1573405620666230531162711

open_access

Abstract

Aim: This study aimed to automatically implement liver disease quantification (DQ) in lymphoma using CT images without lesion segmentation.

Background: Computed Tomography (CT) imaging manifestations of liver lymphoma include diffuse infiltration, blurred boundaries, vascular drift signs, and multiple lesions, making liver lymphoma segmentation extremely challenging.

Methods: The method includes two steps: liver recognition and liver disease quantification. We use the transfer learning technique to recognize the diseased livers automatically and delineate the livers manually using the CAVASS software. When the liver is recognized, liver disease quantification is performed using the disease map model. We test our method in 10 patients with liver lymphoma. A random grouping cross-validation strategy is used to evaluate the quantification accuracy of the manual and automatic methods, with reference to the ground truth.

Results: We split the 10 subjects into two groups based on lesion size. The average accuracy for the total lesion burden (TLB) quantification is 91.76% ± 0.093 for the group with large lesions and 95.57% ± 0.032 for the group with small lesions using the manual organ (MO) method. An accuracy of 85.44% ± 0.146 for the group with larger lesions and 81.94% ± 0.206 for the small lesion group is obtained using the automatic organ (AO) method, with reference to the ground truth.

Conclusion: Our DQ-MO and DQ-AO methods show good performance for varied lymphoma morphologies, from homogeneous to heterogeneous, and from single to multiple lesions in one subject. Our method can also be extended to CT images of other organs in the abdomen for disease quantification, such as Kidney, Spleen and Gallbladder.

Keywords: Disease quantification, Liver lymphoma, Image segmentation, Deep learning, U-Net, Convolutional neutral network, Disease map.

[1]
Feng J, Hu H, Huang B, Chen Z, Yang W, Jin C. MRI and CT features of primary hepatic lymphoma. J Chin Clin Med Imaging 2020; 31: 671-4.
[2]
Guan J, Du F, Wei M, Peng Y, Chen Y. Study on the imaging findings of primary hepatic lymphoma. J Chengdu Med College 2018; 13: 671-6.
[3]
Han D, Bayouth J, Song Q. Globally optimal tumor segmentation in PET-CT images: A graph-based co-segmentation method. In: Székely G, Hahn HK, Eds. Information Processing in Medical Imaging. LNCS, Springer 2011; vol 6801: pp. 245-56.
[http://dx.doi.org/10.1007/978-3-642-22092-0_21]
[4]
Smeets D, Loeckx D, Stijnen B, De Dobbelaer B, Vandermeulen D, Suetens P. Semi-automatic level set segmentation of liver tumors combining a spiral-scanning technique with supervised fuzzy pixel classification. Med Image Anal 2010; 14(1): 13-20.
[http://dx.doi.org/10.1016/j.media.2009.09.002] [PMID: 19828356]
[5]
Hoogi A, Beaulieu CF, Cunha GM, et al. Adaptive local window for level set segmentation of CT and MRI liver lesions. Med Image Anal 2017; 37: 46-55.
[http://dx.doi.org/10.1016/j.media.2017.01.002] [PMID: 28157660]
[6]
Li BN, Chui CK, Chang S, Ong SH. A new unified level set method for semi-automatic liver tumor segmentation on contrast-enhanced CT images. Expert Syst Appl 2012; 39(10): 9661-8.
[http://dx.doi.org/10.1016/j.eswa.2012.02.095]
[7]
Cohen AB, Diamant I, Klang E, Amitai M, Greenspan H. Automatic detection and segmentation of liver metastatic lesions on serial CT examinations. Proc SPIE 2014; 9035: 903519.
[8]
Yan J, Schwartz LH, Zhao B. Semiautomatic segmentation of liver metastases on volumetric CT images. Med Phys 2015; 42(11): 6283-93.
[http://dx.doi.org/10.1118/1.4932365] [PMID: 26520721]
[9]
Baâzaoui A, Barhoumi W, Ahmed A, Zagrouba E. Semi-automated segmentation of single and multiple tumors in liver CT images using entropy-based fuzzy region growing. IRBM 2017; 38(2): 98-108.
[http://dx.doi.org/10.1016/j.irbm.2017.02.003]
[10]
Freiman M, Cooper O, Lischinski D, Joskowicz L. Liver tumors segmentation from CTA images using voxels classification and affinity constraint propagation. Int J CARS 2011; 6(2): 247-55.
[http://dx.doi.org/10.1007/s11548-010-0497-5] [PMID: 20574799]
[11]
Khan M Z, Gajendran M K, Lee Y, Khan M A. deep neural architectures for medical image semantic segmentation: Review. IEEE Access 2021; 9: 83002-24.
[12]
Zheng Z, Sheng VS, Wang L, Li Z, Xi X, Cui Z. SemicNet: a semicircular network for the segmentation of the liver and its lesions. IJSNet 2021; 35(3): 161.
[http://dx.doi.org/10.1504/IJSNET.2021.113838]
[13]
Heker M, Ben A, Greenspan H. Hierarchical fine-tuning for joint liver lesion segmentation and lesion classification in CT. 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 23-27 July; Berlin, Germany. 2019.
[http://dx.doi.org/10.1109/EMBC.2019.8857127]
[14]
Chen L, Song H, Wang C, et al. Liver tumor segmentation in CT volumes using an adversarial densely connected network. BMC Bioinformatics 2019; 20(S16): 587.
[http://dx.doi.org/10.1186/s12859-019-3069-x] [PMID: 31787071]
[15]
Meng L, Tian Y, Bu S. Liver tumor segmentation based on 3D convolutional neural network with dual scale. J Appl Clin Med Phys 2020; 21(1): 144-57.
[http://dx.doi.org/10.1002/acm2.12784] [PMID: 31793212]
[16]
Nanda N, Kakkar P, Nagpal S. Computer-aided segmentation of liver lesions in CT scans using cascaded convolutional neural networks and genetically optimised classifier. Arab J Sci Eng 2019; 44(4): 4049-62.
[http://dx.doi.org/10.1007/s13369-019-03735-8]
[17]
Withofs N, Bernard C, van der Rest C, et al. FDG PET/CT for rectal carcinoma radiotherapy treatment planning: Comparison of functional volume delineation algorithms and clinical challenges. J Appl Clin Med Phys 2014; 15(5): 216-28.
[http://dx.doi.org/10.1120/jacmp.v15i5.4696] [PMID: 25207560]
[18]
Geworski L, Karwarth C, Fitz E, Plotkin M, Knoop B. [Quality control in PET/CT systems: Experiences and requirements]. Z Med Phys 2010; 20(1): 46-50.
[http://dx.doi.org/10.1016/j.zemedi.2009.10.009] [PMID: 20304719]
[19]
Hofheinz F, Poetzsch C. Quantitative 3D ROI delineation in PET: Algorithm and validation. J Nucl Med 2007; 48: 400-7.
[20]
Hofheinz F, Dittrich S, Pötzsch C, Hoff J. Effects of cold sphere walls in PET phantom measurements on the volume reproducing threshold. Phys Med Biol 2010; 55(4): 1099-113.
[http://dx.doi.org/10.1088/0031-9155/55/4/013] [PMID: 20107246]
[21]
Tong Y, Udupa JK, Odhner D, Wu C, Schuster SJ, Torigian DA. Disease quantification on PET/CT images without explicit object delineation. Med Image Anal 2019; 51: 169-83.
[http://dx.doi.org/10.1016/j.media.2018.11.002] [PMID: 30453165]
[22]
Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation. International Conference on Medical image computing and computer-assisted intervention. 2015 OCT 5-9; Munich, Germany. 2015.9351
[http://dx.doi.org/10.1007/978-3-319-24574-4_28]
[23]
Udupa JK, Odhner D, Zhao L, et al. Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images. Med Image Anal 2014; 18(5): 752-71.
[http://dx.doi.org/10.1016/j.media.2014.04.003] [PMID: 24835182]
[24]
Wang H, Udupa JK, Odhner D, Tong Y, Zhao L, Torigian DA. Automatic anatomy recognition in whole-body PET/CT images. Med Phys 2016; 43(1): 613-29.
[http://dx.doi.org/10.1118/1.4939127] [PMID: 26745953]
[25]
MIPG Developed Software. Available from: http://www.mipg.upenn.edu/Vnews/mipg_software.html
[26]
The liver imaging atlas. Available from: http://www.liveratlas.org

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