Automatic atlas-based liver segmental anatomy identification for hepatic surgical planning

Alirr, Omar Ibrahim; Rahni, Ashrani Aizzuddin Abd

doi:10.1007/s11548-019-02078-x

Cited by 13 publications

(17 citation statements)

References 29 publications

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“…Their method was validated by comparing averaged volume of each segment from three test runs against those reported in the literature. Other works [21,25,28] employed some samples from public liver images, but lacking ground truth for functional segments has led to direct comparison being problematic.…”

Section: Methodsmentioning

confidence: 99%

“…3) The view on which a trace was drawn must be consistent, i.e., either axial, coronal, or sagittal. In 2020, the most recent study by Alirr and Rahni [28], connected the vena cava with HV centerlines, defined on individual slices, to build three hepatic planes, while the portal plane was defined by a selected image slice. Therein, the veins bifurcations were located by deforming statistically trained atlas to match extracted vasculature on CT images.…”

Section: B Surface-based Approachmentioning

confidence: 99%

“…These subjective processes caused not only fatigue during batch analyses, but also significant observer variability. A deformable statistical atlas [28] could help elevating manual intervention, provided that sufficiently large training set was available. Unlike other methods in its group, the one, suggested by Boltcheva et al [22], relied solely on the shape of liver, while neglecting its interior vasculature.…”

Section: Summary Of Latest Algorithmsmentioning

confidence: 99%

“…While the former requires its user to submit CT images and then wait for full report, the latter is interactive and thus depends greatly on user's expertise, e.g., significant manual adjustment is required for regions with poor vascular clearance, etc. Therefore, to resolve these limitations, algorithmic enhancement has still remained vital and been an active area of research [18,20,21,24,25,28].…”

Section: ) Evaluation At Functional Section-levelmentioning

confidence: 99%

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Functional Segmentation for Preoperative Liver Resection Based on Hepatic Vascular Networks

Chansangrat

Keeratibharat

et al. 2021

IEEE Access

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Successful liver resection relies on accurate estimation of future liver remnant volume (FLRV). According to Couinaud's scheme, a liver is composed of eight functionally independent segments, each of which has its own vascular in-and out-flow tracks. Segmenting a liver by this scheme is vital to postoperative regeneration and hence prognosis outcome. Conventionally, estimation of liver segments was often done by hand on 3D computed tomography. The process is generally tedious, time consuming, and prone to observer variability. Alternatively, computerized methods had been proposed but impeded by anatomically irrelevant approximation and manually specified markers. To resolve the issues, this paper presents a novel method for functional liver segmentation. Its main contribution was performing analyses of differential geometry directly on a liver surface and interior venous system. Except for a few points being placed on major vessels, anatomical references required for defining all separating surfaces were automatically identified. To demonstrate its merits, virtual liver resection was implemented on the standard MICCAI SLIVER07 dataset, and the resultant segments were benchmarked against four most related works. Visual and numerical assessments reported herein indicated that our method could faithfully label all Couinaud's segments, especially the caudate, with lesser degree of user interaction. The preliminary findings suggested that it can be integrated into augmented surgical planning and intervention.

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Section: Methodsmentioning

confidence: 99%

Section: B Surface-based Approachmentioning

confidence: 99%

Section: Summary Of Latest Algorithmsmentioning

confidence: 99%

Section: ) Evaluation At Functional Section-levelmentioning

confidence: 99%

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Functional Segmentation for Preoperative Liver Resection Based on Hepatic Vascular Networks

Chansangrat

Keeratibharat

et al. 2021

IEEE Access

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“…An automatic method has been used to quantitatively assess the registration of retinal images based on the extraction of similar vessel structures and a modified Hausdorff distance [20] . In the preoperative planning for liver surgical resection treatment, point-based registration is used to deform the mesh of the atlas to the vein branch [21,22]. To evaluate fusion accuracy, Riva et al divided anatomical landmark pairs into test and control structures based on distinct involvement with brain shift [23].…”

Section: Introductionmentioning

confidence: 99%

Anatomical Point-of-Interest Detection in Head MRI Using Multipoint Feature Descriptor

Chen

et al. 2020

IEEE Access

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Automatic detection of specific anatomical points of interest (POIs) in head magnetic resonance imaging (MRI) is a technical bottleneck in medical image registration and big data analysis for head diseases. A technique for automatically retrieving POIs in head MRI scans is explored in this study. A Haar-like feature of the image is introduced to generate the feature description vector of the POI, and the most appropriate standard vector is selected from multiple marked images. A fixed five-point joint feature description is proposed for solid POI detection, and an adaptive three-point joint feature description is proposed for cavity POI detection. A total of 516 head MRI volumes were used for anatomical point detection. A solid point detection experiment was conducted using the POIs of the right/left internal acoustic pore (RIA/LIA). The POIs of the right/left ascending segment of the internal carotid artery in the posterior cavernous sinus (RAS/LAS) were used in a cavity point detection experiment. The experimental results show that the accuracies of the solid point detection for LIA and RIA are 81.8% and 84.7%, respectively. Those of cavity point detection for LAS and RAS are 66.7% and 76.2%. The performance of the proposed method is better than those of BRIEF and SIFT algorithm. The proposed method can facilitate the marking of anatomical points for doctors, thus providing technical support for head image automatic registration and big data analysis for head diseases.

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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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Automatic atlas-based liver segmental anatomy identification for hepatic surgical planning

Cited by 13 publications

References 29 publications

Functional Segmentation for Preoperative Liver Resection Based on Hepatic Vascular Networks

Functional Segmentation for Preoperative Liver Resection Based on Hepatic Vascular Networks

Anatomical Point-of-Interest Detection in Head MRI Using Multipoint Feature Descriptor

Hepatic vessels segmentation using deep learning and preprocessing enhancement

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