Survey on Liver Tumour Resection Planning System: Steps, Techniques, and Parameters

Alirr, Omar Ibrahim; Rahni, Ashrani Aizzuddin Abd

doi:10.1007/s10278-019-00262-8

Cited by 12 publications

(6 citation statements)

References 83 publications

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“…Medical imaging using computed tomography (CT) scans is the most commonly used imaging techniques for liver cancer diagnosis, since it gives accurate anatomical information about the abdominal organs in the human body 2 . Liver organ and tumor segmentation from CT scans is an important step in the visualization of liver anatomy for surgical planning 3 …”

Section: Introductionmentioning

confidence: 99%

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Deep learning and level set approach for liver and tumor segmentation from CT scans

Alirr

2020

J Applied Clin Med Phys

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Purpose Segmentation of liver organ and tumors from computed tomography (CT) scans is an important task for hepatic surgical planning. Manual segmentation of liver and tumors is tedious, time‐consuming, and biased to the clinician experience. Therefore, automatic segmentation of liver and tumors is highly desirable. It would improve the surgical planning treatments and follow‐up assessment. Method This work presented the development of an automatic method for liver and tumor segmentation from CT scans. The proposed method was based on fully convolutional neural (FCN) network with region‐based level set function. The framework starts to segment the liver organ from CT scan, which is followed by a step to segment tumors inside the liver envelope. The fully convolutional network is trained to predict the coarse liver/tumor segmentation, while the localized region‐based level aims to refine the predicted segmentation to find the correct final segmentation. Results The effectiveness of the proposed method is validated against two publically available datasets, LiTS and IRCAD datasets. Dice scores for liver and tumor segmentation in IRCAD datasets are 95.2% and 76.1%, respectively, while for LiTS dataset are 95.6% and 70%, respectively. Conclusion The proposed method succeeded to segment liver and tumors in heterogeneous CT scans from different scanners, as in IRCAD dataset, which proved its ability for generalization and be promising tool for automatic analysis of liver and its tumors in clinical routine.

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Section: Introductionmentioning

confidence: 99%

“…2 Liver organ and tumor segmentation from CT scans is an important step in the visualization of liver anatomy for surgical planning. 3 Manual segmentation of liver organ and tumors from CT scans is tedious and time-consuming. It greatly depends on the skills of the physician or doctor who performs the segmentation task.…”

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confidence: 99%

Deep learning and level set approach for liver and tumor segmentation from CT scans

Alirr

2020

J Applied Clin Med Phys

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“…A very high ICC above 0.9 for all analyzed volumes and a proven equivalence for all sectors and territories demonstrated a good quality of liver segmentation images in the current study. The variance in segmental volumes can be explained by the naturally smooth transitions between segments in general and very thin vessels at the end of the branches [18]. In areas with very small vessels that are difficult to reconstruct especially segment 1 and segment 4a/b, a higher variation was observed.…”

Section: Discussionmentioning

confidence: 99%

“…In areas with very small vessels that are difficult to reconstruct especially segment 1 and segment 4a/b, a higher variation was observed. The selection of the feeding vessel and the associated liver volume can also be subjected to human errors and subjective interpretation [18]. Especially for the right hepatic lobe, the assignment of smaller vessels at the borders of the segments can be demanding.…”

Section: Discussionmentioning

confidence: 99%

Local, semi-automatic, three-dimensional liver reconstruction or external provider? An analysis of performance and time expense

Paschold

Huettl

Kneist

et al. 2020

Langenbecks Arch Surg

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Purpose In hepatobiliary surgery, preoperative three-dimensional reconstruction based on CT or MRI can be provided externally or by local, semi-automatic software. We analyzed the time expense and quality of external versus local three-dimensional reconstructions. Methods Three first-year residents reconstructed data from 20 patients with liver pathologies using a local, semi-automatic, server-based program. Initially, five randomly selected patient datasets were segmented, with the visualization of an established external company available for comparison at all times (learning phase). The other fifteen cases were compared with the external datasets after completing local reconstruction (control phase). Total time expense/case and for specific manual and semiautomated reconstruction steps were recorded. Segmentation quality was analyzed by testing the equivalence for liver and tumor volumes, portal vein sectors, and hepatic vein territories. Results The median total reconstruction time was reduced from 2.5 h (learning phase) to 1.5 h (control phase) (− 42%; p < 0.001). Comparing the total and detailed liver volumes (sectors and territories) as well as the tumor volumes in the control phase equivalence was proven. In addition, a highly significant correlation between the external and local analysis was obtained over all analyzed segments with a very high ICC (median [IQR]: 0.98 [0.97; 0.99]; p < 0.01). Conclusion Local, semi-automatic reconstruction performed by inexperienced residents was feasible with an expert level time expense and the quality of the three-dimensional images was comparable with those from an external provider.

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“…3 The segmentation of the liver's vasculature is a crucial stage in computer-aided systems used in liver surgery. 4,5 Vessel information extraction from 3D pictures has attracted a lot of attention due to its significance in numerous medical applications. For vessel segmentation, numerous segmentation methods have been suggested.…”

Section: Introductionmentioning

confidence: 99%

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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Survey on Liver Tumour Resection Planning System: Steps, Techniques, and Parameters

Cited by 12 publications

References 83 publications

Deep learning and level set approach for liver and tumor segmentation from CT scans

Deep learning and level set approach for liver and tumor segmentation from CT scans

Local, semi-automatic, three-dimensional liver reconstruction or external provider? An analysis of performance and time expense

Hepatic vessels segmentation using deep learning and preprocessing enhancement

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