Hepatic vessels segmentation using deep learning and preprocessing enhancement

Alirr, Omar Ibrahim; Rahni, Ashrani Aizzuddin Abd

doi:10.1002/acm2.13966

Cited by 11 publications

(1 citation statement)

References 45 publications

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“…2,3 Due to the intricate hepatic anatomy, even experienced surgeons can be blinded to some critical structures, potentially affecting surgical decision-making. 4 Accurate information acquisition on the liver and hepatic vessels from CT images greatly facilitates preoperative planning of hepatectomy, liver transplantation, and minimally invasive therapies. [5][6][7] Prior liver segmentation mainly concentrates on contour structures and volumetric measurements, while the segmentation for both internal anatomy and vascular architecture remains limited.…”

Section: Introductionmentioning

confidence: 99%

Automated segmentation of liver and hepatic vessels on portal venous phase computed tomography images using a deep learning algorithm

Li,

Zhou

et al. 2024

J Applied Clin Med Phys

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BackgroundCT‐image segmentation for liver and hepatic vessels can facilitate liver surgical planning. However, time‐consuming process and inter‐observer variations of manual segmentation have limited wider application in clinical practice.PurposeOur study aimed to propose an automated deep learning (DL) segmentation algorithm for liver and hepatic vessels on portal venous phase CT images.MethodsThis retrospective study was performed to develop a coarse‐to‐fine DL‐based algorithm that was trained, validated, and tested using private 413, 52, and 50 portal venous phase CT images, respectively. Additionally, the performance of the DL algorithm was extensively evaluated and compared with manual segmentation using an independent clinical dataset of preoperative contrast‐enhanced CT images from 44 patients with hepatic focal lesions. The accuracy of DL‐based segmentation was quantitatively evaluated using the Dice Similarity Coefficient (DSC) and complementary metrics [Normalized Surface Dice (NSD) and Hausdorff distance_95 (HD95) for liver segmentation, Recall and Precision for hepatic vessel segmentation]. The processing time for DL and manual segmentation was also compared.ResultsOur DL algorithm achieved accurate liver segmentation with DSC of 0.98, NSD of 0.92, and HD95 of 1.52 mm. DL‐segmentation of hepatic veins, portal veins, and inferior vena cava attained DSC of 0.86, 0.89, and 0.94, respectively. Compared with the manual approach, the DL algorithm significantly outperformed with better segmentation results for both liver and hepatic vessels, with higher accuracy of liver and hepatic vessel segmentation (all p < 0.001) in independent 44 clinical data. In addition, the DL method significantly reduced the manual processing time of clinical postprocessing (p < 0.001).ConclusionsThe proposed DL algorithm potentially enabled accurate and rapid segmentation for liver and hepatic vessels using portal venous phase contrast CT images.

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Section: Introductionmentioning

confidence: 99%

Automated segmentation of liver and hepatic vessels on portal venous phase computed tomography images using a deep learning algorithm

Li,

Zhou

et al. 2024

J Applied Clin Med Phys

View full text Add to dashboard Cite

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Hepatic vessels segmentation using deep learning and preprocessing enhancement

Alirr

Rahni

2023

J Applied Clin Med Phys

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Liver hepatic vessels segmentation is a crucial step for the diagnosis process in patients with hepatic diseases. Segmentation of liver vessels helps to study the liver internal segmental anatomy that helps in the preoperative planning of surgical treatment. Methods: Recently, the convolutional neural networks (CNN) have been proved to be efficient for the task of medical image segmentation. The paper proposes an automatic deep learning-based system for liver hepatic vessels segmentation of Computed Tomography (CT) datasets from different sources. The proposed work focuses on the combination of different steps; it starts by a preprocessing step to improve the vessels appearance within the liver region of interest in the CT scans. Coherence enhancing diffusion filtering (CED) and vesselness filtering methods are used to improve vessels contrast and intensity homogeneity. The proposed U-net based network architecture is implemented with modified residual block to include concatenation skip connection. The effect of enhancement using filtering step was studied. Also, the effect of data mismatch used in training and validation is studied. Results: The proposed method is evaluated using many CT datasets. Dice similarity coefficient (DSC) is used to evaluate the method. The average DSC score achieved a score 79%. Conclusions: The proposed approach succeeded to segment liver vasculature from the liver envelope accurately, which makes it as potential tool for clinical preoperative planning.

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Semantic Segmentation of CT Liver Structures: A Systematic Review of Recent Trends and Bibliometric Analysis

Delmoral,

R.S. Tavares

2024

J Med Syst

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The use of artificial intelligence (AI) in the segmentation of liver structures in medical images has become a popular research focus in the past half-decade. The performance of AI tools in screening for this task may vary widely and has been tested in the literature in various datasets. However, no scientometric report has provided a systematic overview of this scientific area. This article presents a systematic and bibliometric review of recent advances in neuronal network modeling approaches, mainly of deep learning, to outline the multiple research directions of the field in terms of algorithmic features. Therefore, a detailed systematic review of the most relevant publications addressing fully automatic semantic segmenting liver structures in Computed Tomography (CT) images in terms of algorithm modeling objective, performance benchmark, and model complexity is provided. The review suggests that fully automatic hybrid 2D and 3D networks are the top performers in the semantic segmentation of the liver. In the case of liver tumor and vasculature segmentation, fully automatic generative approaches perform best. However, the reported performance benchmark indicates that there is still much to be improved in segmenting such small structures in high-resolution abdominal CT scans.

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Hepatic vessels segmentation using deep learning and preprocessing enhancement

Cited by 11 publications

References 45 publications

Automated segmentation of liver and hepatic vessels on portal venous phase computed tomography images using a deep learning algorithm

Automated segmentation of liver and hepatic vessels on portal venous phase computed tomography images using a deep learning algorithm

Hepatic vessels segmentation using deep learning and preprocessing enhancement

Semantic Segmentation of CT Liver Structures: A Systematic Review of Recent Trends and Bibliometric Analysis

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