Liver tumors segmentation from CTA images using voxels classification and affinity constraint propagation

Freiman, Moti; Cooper, Ofir; Lischinski, Dani; Joskowicz, Leo

doi:10.1007/s11548-010-0497-5

Cited by 39 publications

(14 citation statements)

References 23 publications

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“…Hame (Hame and Pollari, 2012) interactively classified CT images into tumor and non-tumor regions, and refined tumor regions using Hidden Markov fields. Support vector machine and affinity constraint propagation were explored to semi-automatically segment hepatic tumors in (Freiman et al, 2011). Recently, Linguraru (Linguraru et al, 2012b) developed a fully automated tumor segmentation algorithm by comparing the segmented liver with a sequence of liver atlases to identify tumors along liver boundaries through shape analysis.…”

Section: Related Workmentioning

confidence: 99%

Tumor sensitive matching flow: A variational method to detecting and segmenting perihepatic and perisplenic ovarian cancer metastases on contrast-enhanced abdominal CT

Liu

Wang

Linguraru

et al. 2014

Medical Image Analysis

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Accurate automated segmentation and detection of ovarian cancer metastases may improve the diagnosis and prognosis of women with ovarian cancer. In this paper, we focus on an important subset of ovarian cancer metastases that spread to the surface of the liver and spleen. Automated ovarian cancer metastasis detection and segmentation are very challenging problems to solve. These metastases have a wide variety of shapes and intensity values similar to that of the liver, spleen and adjacent soft tissues. To address these challenges, this paper presents a variational approach, called tumor sensitive matching flow (TSMF), to detect and segment perihepatic and perisplenic ovarian cancer metastases. TSMF is an image motion field that only highlights metastasis-caused deformation on the surface of liver and spleen while dampening all other image motion between the patient image and the atlas image. It provides several benefits: 1) juxtaposing the roles of image matching and metastasis classification within a variational framework; 2) only requiring a small set of features from a few patient images to train a metastasis-likelihood function for classification; and 3) dynamically creating shape priors for geodesic active contour (GAC) to prevent inaccurate metastasis segmentation. We compared the TSMF to an organ surface partition (OSP) baseline approach. At a false positive rate of 2 per patient, the sensitivities of TSMF and OSP were 87% and 17% (p < 0.001), respectively. In a comparison of the segmentations conducted using TSMF-constrained GAC and conventional GAC, the volume overlap rates were 73±9% and 46±26% (p < 0.001) and average surface distances were 2.4±1.2mm and 7.0±6.0mm (p < 0.001), respectively. These encouraging results demonstrate that TSMF could accurately detect and segment ovarian cancer metastases.

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Section: Related Workmentioning

confidence: 99%

Tumor sensitive matching flow: A variational method to detecting and segmenting perihepatic and perisplenic ovarian cancer metastases on contrast-enhanced abdominal CT

Liu

Wang

Linguraru

et al. 2014

Medical Image Analysis

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“…Freiman et al [20] evaluated the performance of their proposed scheme on CTA images, and it could obtain a volume overlap error and a volume difference of 33.8% and 22.6%, respectively. The mean of average surface distance, the RMS surface distance, and the maximal surface distance were 1.76 mm, 2.62 mm, and 13.73 mm, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…Our proposed scheme was evaluated in 16 patients with 25 tumors while other studies evaluated 5 patients with 10 tumors [20], 7 patients with 10 tumors [15], and 10 patients with 10 lesions [30]. In general, a small number of tumors and patients may limit the variations among them.…”

Section: Resultsmentioning

confidence: 99%

“…The user-defined seed point is required, and the additional seed points are generated from 2D fuzzy-connectedness segmentation of a slice containing the seed point. Freiman et al [20] developed an approach for liver tumor segmentation from CTA using SVM, in which the features include the mean, the standard deviation, and the minimum and the maximum intensity values in a predefined window surrounding the voxel. Huang et al [21] demonstrated an approach for liver tumor segmentation based on extreme learning machine.…”

Section: Introductionmentioning

confidence: 99%

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Liver Tumor Segmentation from MR Images Using 3D Fast Marching Algorithm and Single Hidden Layer Feedforward Neural Network

Le¹,

Huynh

2016

BioMed Research International

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Objective. Our objective is to develop a computerized scheme for liver tumor segmentation in MR images. Materials and Methods. Our proposed scheme consists of four main stages. Firstly, the region of interest (ROI) image which contains the liver tumor region in the T1-weighted MR image series was extracted by using seed points. The noise in this ROI image was reduced and the boundaries were enhanced. A 3D fast marching algorithm was applied to generate the initial labeled regions which are considered as teacher regions. A single hidden layer feedforward neural network (SLFN), which was trained by a noniterative algorithm, was employed to classify the unlabeled voxels. Finally, the postprocessing stage was applied to extract and refine the liver tumor boundaries. The liver tumors determined by our scheme were compared with those manually traced by a radiologist, used as the “ground truth.” Results. The study was evaluated on two datasets of 25 tumors from 16 patients. The proposed scheme obtained the mean volumetric overlap error of 27.43% and the mean percentage volume error of 15.73%. The mean of the average surface distance, the root mean square surface distance, and the maximal surface distance were 0.58 mm, 1.20 mm, and 6.29 mm, respectively.

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“…Freiman et al [32] presented an automatic segmentation method to segment the tumors in the liver from CTA scan. It first classifies the liver voxels into the tumor and healthy tissue classes with an SVM classification engine from which a new set of high-quality seeds is generated.…”

Section: Related Workmentioning

confidence: 99%

Medical Image Segmentation for Liver Diseases: A Survey

Hassan¹,

Elmogy²,

Sallam³

2015

IJCA

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Liver tumors segmentation from CTA images using voxels classification and affinity constraint propagation

Abstract: Our results indicate that our method is accurate, efficient, and robust to wide variety of tumor types and is comparable or superior to other semi-automatic segmentation methods, with much less user interaction.

Cited by 39 publications

References 23 publications

Tumor sensitive matching flow: A variational method to detecting and segmenting perihepatic and perisplenic ovarian cancer metastases on contrast-enhanced abdominal CT

Tumor sensitive matching flow: A variational method to detecting and segmenting perihepatic and perisplenic ovarian cancer metastases on contrast-enhanced abdominal CT

Liver Tumor Segmentation from MR Images Using 3D Fast Marching Algorithm and Single Hidden Layer Feedforward Neural Network

Medical Image Segmentation for Liver Diseases: A Survey

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