3D Active Shape Model for CT-scan liver segmentation

Trabelsi, Nesrine; Aloui, Karam; Sellem, D. Ben

doi:10.1109/setit.2016.7939859

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…Our contribution is to improve the performance of the 3D ASM algorithm with a pre-processing phase using a non rigid registration based on B-Spline transformation. The following paper is an extension of work originally presented in the 7th International Conference on Sciences of Electronics, Technologies of Information Telecommunication" [1]. In this work, we improve the evaluation of results using the ROC-Curve analysis and we ensure the reliability of our method using our database in addition to the "3D-IRCADb".…”

Section: Introductionmentioning

confidence: 95%

Non-rigid Registration for 3D Active Shape Liver Modeling

Trabelsi¹,

Cherni²,

Sellem³

2018

Adv. sci. technol. eng. syst. j.

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To avoid biopsies, doctors use non invasive medical techniques such as the computed tomography. Even that, the detection of the liver remains a big challenge because of the gray level and shape variations which depend on patients and acquisition modalites. In this work, we propose to create a 3D liver model in the training phase of 3D active shape model algorithm. This training model will be deformed according to any given 3D data for liver segmentation. The contribution of our work is the use of the Non-rigid registration with a B-spline registration on the training phase. We tested our method on an open access database ("3D-IRCADb") and on our database obtained from the radiology department of the National Oncology Institute of Tunis. Both data-sets showed the reliability of the method with an accuracy equal to 69.98% and 71.18% respectively for our database and "3D-IRCADb".

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Section: Introductionmentioning

confidence: 95%

Non-rigid Registration for 3D Active Shape Liver Modeling

Trabelsi¹,

Cherni²,

Sellem³

2018

Adv. sci. technol. eng. syst. j.

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“…A parallel algorithm was designed for further refining the segmentation. Trabelsi et al 30 proposed an active shape model to obtain the liver region. B-spline registration was first applied to obtain the initial liver region.…”

Section: Introductionmentioning

confidence: 99%

A hybrid approach based on deep learning and level set formulation for liver segmentation in CT images

Gong

Guo

et al. 2021

J Applied Clin Med Phys

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Accurate liver segmentation is essential for radiation therapy planning of hepatocellular carcinoma and absorbed dose calculation. However, liver segmentation is a challenging task due to the anatomical variability in both shape and size and the low contrast between liver and its surrounding organs. Thus we propose a convolutional neural network (CNN) for automated liver segmentation. In our method, fractional differential enhancement is firstly applied for preprocessing. Subsequently, an initial liver segmentation is obtained by using a CNN. Finally, accurate liver segmentation is achieved by the evolution of an active contour model. Experimental results show that the proposed method outperforms existing methods. One hundred fifty CT scans are evaluated for the experiment. For liver segmentation, Dice of 95.8%, true positive rate of 95.1%, positive predictive value of 93.2%, and volume difference of 7% are calculated. In addition, the values of these evaluation measures show that the proposed method is able to provide a precise and robust segmentation estimate, which can also assist the manual liver segmentation task.

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