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. 2022 May 24;22(1):97.
doi: 10.1186/s12880-022-00825-2.

Practical utility of liver segmentation methods in clinical surgeries and interventions

Mohammed Yusuf Ansari  1 , Alhusain Abdalla  2 , Mohammed Yaqoob Ansari  3 , Mohammed Ishaq Ansari  3 , Byanne Malluhi  3 , Snigdha Mohanty  4 , Subhashree Mishra  4 , Sudhansu Sekhar Singh  4 , Julien Abinahed  1 , Abdulla Al-Ansari  1 , Shidin Balakrishnan  1 , Sarada Prasad Dakua  5
Affiliations

Practical utility of liver segmentation methods in clinical surgeries and interventions

Mohammed Yusuf Ansari et al. BMC Med Imaging. .

Erratum in

Abstract

Clinical imaging (e.g., magnetic resonance imaging and computed tomography) is a crucial adjunct for clinicians, aiding in the diagnosis of diseases and planning of appropriate interventions. This is especially true in malignant conditions such as hepatocellular carcinoma (HCC), where image segmentation (such as accurate delineation of liver and tumor) is the preliminary step taken by the clinicians to optimize diagnosis, staging, and treatment planning and intervention (e.g., transplantation, surgical resection, radiotherapy, PVE, embolization, etc). Thus, segmentation methods could potentially impact the diagnosis and treatment outcomes. This paper comprehensively reviews the literature (during the year 2012-2021) for relevant segmentation methods and proposes a broad categorization based on their clinical utility (i.e., surgical and radiological interventions) in HCC. The categorization is based on the parameters such as precision, accuracy, and automation.

Keywords: Intervention; Liver; Segmentation; Surgery; Tumor.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Examples of challenges in liver segmentation: a ambiguous boundary between liver and stomach, b ambiguous boundary between liver and heart, c similar intensity of liver and tumor
Fig. 2
Fig. 2
Applications of segmentation methods for liver diseases
Fig. 3
Fig. 3
Staging classification and treatment algorithm of very early (0) and early (A) stage HCC based on BCLC criteria
Fig. 4
Fig. 4
Structural summary of section 3 and 4, highlighting the essential functionalities of segmentation methods for radiological and surgical interventions
Fig. 5
Fig. 5
a Raw CT slice and b Segmented liver
Fig. 6
Fig. 6
Technical and clinical challenges facing diagnosis and treatment of HCC
See this image and copyright information in PMC

References

    1. Organization WH, et al. Who report on cancer: setting priorities, investing wisely and providing care for all 2020.
    1. Campadelli P, Casiraghi E, Esposito A. Liver segmentation from computed tomography scans: a survey and a new algorithm. Artif Intell Med. 2009;45(2–3):185–196. doi: 10.1016/j.artmed.2008.07.020. - DOI - PubMed
    1. Norouzi A, Rahim MSM, Altameem A, Saba T, Rad AE, Rehman A, Uddin M. Medical image segmentation methods, algorithms, and applications. IETE Tech Rev. 2014;31(3):199–213. doi: 10.1080/02564602.2014.906861. - DOI
    1. Jayadevappa D, Srinivas Kumar S, Murty D. Medical image segmentation algorithms using deformable models: a review. IETE Tech Rev. 2011;28(3):248–255. doi: 10.4103/0256-4602.81244. - DOI
    1. Bilic P, Christ PF, Vorontsov E, Chlebus G, Chen H, Dou Q, Fu C-W, Han X, Heng P-A, Hesser J, et al. The liver tumor segmentation benchmark (lits). arXiv preprint arXiv:1901.04056 2019

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