An automated liver tumour segmentation from abdominal CT scans for hepatic surgical planning

Alirr, Omar Ibrahim; Rahni, Ashrani Aizzuddin Abd; Golkar, Ehsan

doi:10.1007/s11548-018-1801-z

Cited by 20 publications

(16 citation statements)

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“…Step of intensity windowing is applied to exclude irrelevant organs and focus on liver organ intensity range. Based on intensitybased techniques, [16][17][18][19][20] liver organ and tumors Hounsfield (HU) intensities range is 0-200. In this work, HU windowing is applied on datasets used in the FCN training step for both liver and tumor segmentations, the used HU window is −50-250.…”

Section: B | Data Preparationmentioning

confidence: 99%

“…One of these techniques that can be used more independently is the level set‐based active contour methods 18 . Level set‐based active contour method is used to deform an initial mask, that is coarse segmentation, to match more accurately the boundary of the liver/tumor in the test CT scan 19,20 …”

Section: Introductionmentioning

confidence: 99%

“…18 Level set-based active contour method is used to deform an initial mask, that is coarse segmentation, to match more accurately the boundary of the liver/tumor in the test CT scan. 19,20 In this work, two cascaded FCN networks are constructed using U-Net, the same work proposed by Christ et al 21 The first subnetwork aims to locate and predict the liver organ, while the second subnetwork work on the segmented liver envelope to detect and segment tumors. The output of these networks represents the coarse segmentation of liver and tumors, that are considered as initial mask used by the level set method to be deformed to the liver/ tumor boundaries in the target CT image and generate the final segmentation.…”

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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: B | Data Preparationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…Some denoising methods may be helpful to remove these false positive and false negative issues. For example, the edgeenhancing diffusion filtering technique proposed in [25] may help further enhance the segmentation ability of our proposed pipeline.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning Initialized and Gradient Enhanced Level-Set Based Segmentation for Liver Tumor From CT Images

Zhang

Jiang

et al. 2020

IEEE Access

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Liver and liver tumor segmentation provides vital biomarkers for surgical planning and hepatic diagnosis. In this paper, we propose and validate a novel level-set method integrating an enhanced edge indicator and an automatically derived initial curve for CT based liver tumor segmentation. At the preprocessing step, the CT image intensity values were truncated to lie in a fixed range to enhance the image contrast surrounding liver and liver tumor. To remove non-liver tissues for subsequent tumor segmentation, liver was firstly segmented using two convolutional neural networks in a coarse-to-fine manner. A 2D slicebased U-net was used to roughly localize the liver and a 3D patch-based fully convolutional network was used to refine the liver segmentation as well as to roughly localize the liver tumor. A novel level-set method was then presented to further refine the tumor segmentation. Specifically, the probabilistic distribution of the liver tumor was estimated using unsupervised fuzzy c-means clustering, which was then utilized to enhance the edge-detector used in level-set. Effectiveness of the proposed pipeline was validated on two publiclyavailable datasets. Experimental results identified the superior segmentation performance of the proposed pipeline over state-of-the-art methods.

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“…2005) based on bilateral filtering to achieve Monte Carlo method on the GPU to achieve noise reduction. (Liu et al 2018) implemented an improved 3D U-net architecture, which achieves a more precise segmentation effect, (Alirr et al 2018)introduced a fully automated liver tumor segmentation method in the contrast-enhanced CT dataset. (Lebre et al, 2018) proposes an automatic three-dimensional skeleton segmentation method based on bones.…”

Section: Related Workmentioning

confidence: 99%

Automated Extraction of Liver Outlines From Computed Tomography Scan Images Using a Cuda-Based Segmentation Method

Chen

Zhu

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> The traditional fast marching algorithm for segmentation of the liver is suitable for processing on the central processing unit (CPU) platform, however, it is not suitable for implementation on Graphics Processing Unit (GPU). The fuzzy connection algorithm is used to extract the blood vessels in the liver, but there is a calculation error. The refinement algorithm is very time consuming when extracting the target skeleton line from the 3D image. In this paper, the fast-marching algorithm and the thinning algorithm are improved, which can be applied to the GPU computing, The fuzzy algorithm is also improved, and the calculation error of the algorithm is solved, making it more suitable for medical image processing. The computing speed of GPU is far faster than CPU. Medical image processing is one of the earliest applications where the computing performance is improved by GPU. These three segmentation methods, fast marching method, fuzzy connecting method and refinement algorithm are very common in medical image segmentation. Because the increment of medical image data results in the extension of computing time for medical image processing, it is necessary to apply the high parallelism of the GPU to speed up these algorithms. The experiment results demonstrate the feasibility of our accelerating algorithm.</p>

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An automated liver tumour segmentation from abdominal CT scans for hepatic surgical planning

Abstract: The proposed approach aims to segment tumours inside the liver envelope automatically with a level of accuracy adequate for its use as a tool for surgical planning using abdominal CT images. The approach will be validated on larger datasets in the future.

Cited by 20 publications

References 18 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

Deep Learning Initialized and Gradient Enhanced Level-Set Based Segmentation for Liver Tumor From CT Images

Automated Extraction of Liver Outlines From Computed Tomography Scan Images Using a Cuda-Based Segmentation Method

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