Liver segmentation for contrast-enhanced MR images using partitioned probabilistic model

Ruskó, László; Bekes, György

doi:10.1007/s11548-010-0493-9

Cited by 22 publications

(22 citation statements)

References 8 publications

(10 reference statements)

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“…The performance of their method was evaluated on eighteen normal and nine abnormal cases. Ruskó, et al [22] proposed a scheme to represent the liver based on a partitioned probabilistic model. In this method, the liver was partitioned into multiple regions, and different intensity statistical models were applied to these regions.…”

Section: Introductionmentioning

confidence: 99%

Fully automatic scheme for measuring liver volume in 3D MR images

Huynh

2015

BME

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Abstract. In this paper, a fully automatic scheme for measuring liver volume in 3D MR images was developed. The proposed MRI liver volumetry scheme consisted of four main stages. First, the preprocessing stage was applied to T1-weighted MR images of the liver in the portal-venous phase to reduce noise. The histogram of the 3D image was determined, and the second-to-last peak of the histogram was calculated using a neural network. Thresholds, which are determined based upon the second-to-last peak, were used to generate a thresholding image. This thresholding image was refined using a gradient magnitude image. The morphological and connected component operations were applied to the refined image to generate the rough shape of the liver. A 3D geodesic-active-contour segmentation algorithm refined the rough shape in order to more precisely determine the liver boundaries. The liver volumes determined by the proposed automatic volumetry were compared to those manually traced by radiologists; these manual volumes were used as a "gold standard." The two volumetric methods reached an excellent agreement. The Dice overlap coefficient and the average accuracy were 91.0 ±2.8% and 99.0 ±0.4%, respectively. The mean processing time for the proposed automatic scheme was 1.02 ±0.08 min (CPU: Intel, core i7, 2.8GHz), whereas that of the manual volumetry was 24.3 ±3.7 min (p < 0.001).

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Section: Introductionmentioning

confidence: 99%

Fully automatic scheme for measuring liver volume in 3D MR images

Huynh

2015

BME

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“…In addition, the author demonstrated the results are significantly less accurate (VOE=23%), when the liver model is not partitioned. These results were published in journal paper [2]. The author performed an extensive comparison of the three techniques using a large set of liver CT exams including normal as well as extreme (in terms of size or disease) cases.…”

Section: Discussionmentioning

confidence: 99%

“…The results showed the proposed algorithm can accurately segment the liver in short time despite the significant intensity variation that is characteristic for MR images. The results of this work were published in a journal paper [2] and the proposed approach was patented [13].…”

Section: Model-based Methods For Mr Imagesmentioning

confidence: 99%

“…It incorporates a probabilistic liver model as additional information about the organ. This technique was published in another journal paper [2]. Each method was originally evaluated using different test images, which makes their comparison difficult.…”

Section: Liver Segmentationmentioning

confidence: 99%

“…The third method was developed for contrast-enhanced MR images. This technique was published in a journal paper [2]. Each method was evaluated on different datasets, which makes their comparison difficult.…”

Section: Liver Segmentationmentioning

confidence: 99%

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Automated segmentation methods for liver analysis in oncology applications

Ruskó¹

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The error analysis of Lobular and segmental division of right liver by volume measurement

et al. 2017

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The aim of this study is to explore the inconsistencies between right liver volume as measured by imaging and the actual anatomical appearance of the right lobe. Five healthy donated livers were studied. The liver slices were obtained with hepatic segments multicolor-infused through the portal vein. In the slices, the lobes were divided by two methods: radiological landmarks and real anatomical boundaries. The areas of the right anterior lobe (RAL) and right posterior lobe (RPL) on each slice were measured using Photoshop CS5 and AutoCAD, and the volumes of the two lobes were calculated. There was no statistically significant difference between the volumes of the RAL or RPL as measured by the radiological landmarks (RL) and anatomical boundaries (AB) methods. However, the curves of the square error value of the RAL and RPL measured using CT showed that the three lowest points were at the cranial, intermediate, and caudal levels. The U- or V-shaped curves of the square error rate of the RAL and RPL revealed that the lowest value is at the intermediate level and the highest at the cranial and caudal levels. On CT images, less accurate landmarks were used to divide the RAL and RPL at the cranial and caudal layers. The measured volumes of hepatic segments VIII and VI would be less than their true values, and the measured volumes of hepatic segments VII and V would be greater than their true values, according to radiological landmarks. Clin. Anat. 30:585-590, 2017. © 2017 Wiley Periodicals, Inc.

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Liver segmentation for contrast-enhanced MR images using partitioned probabilistic model

Abstract: An automatic method that can segment the liver in contrast-enhanced MR LAVA images was developed and tested. The results demonstrate that the method is feasible, efficient and robust to artifacts and pathology.

Cited by 22 publications

References 8 publications

Fully automatic scheme for measuring liver volume in 3D MR images

Fully automatic scheme for measuring liver volume in 3D MR images

Automated segmentation methods for liver analysis in oncology applications

The error analysis of Lobular and segmental division of right liver by volume measurement

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