Semi-automatic liver segmentation based on probabilistic models and anatomical constraints

AbstractSegmenting a liver and its peripherals from abdominal computed tomography is a crucial step toward computer aided diagnosis and therapeutic intervention. Despite the recent advances in computing methods, faithfully segmenting the liver has remained a challenging task, due to indefinite boundary, intensity inhomogeneity, and anatomical variations across subjects. In this paper, a semi-automatic segmentation method based on multivariable normal distribution of liver tissues and graph-cut sub-division is presented. Although it is not fully automated, the method minimally involves human interactions. Specifically, it consists of three main stages. Firstly, a subject specific probabilistic model was built from an interior patch, surrounding a seed point specified by the user. Secondly, an iterative assignment of pixel labels was applied to gradually update the probabilistic map of the tissues based on spatio-contextual information. Finally, the graph-cut model was optimized to extract the 3D liver from the image. During post-processing, overly segmented nodal regions due to fuzzy tissue separation were removed, maintaining its correct anatomy by using robust bottleneck detection with adjacent contour constraint. The proposed system was implemented and validated on the MICCAI SLIVER07 dataset. The experimental results were benchmarked against the state-of-the-art methods, based on major clinically relevant metrics. Both visual and numerical assessments reported herein indicated that the proposed system could improve the accuracy and reliability of asymptomatic liver segmentation.

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Detection of liver cirrhosis in standard T2-weighted MRI using deep transfer learning

European Radiology ◽

10.1007/s00330-021-07858-1 ◽

2021 ◽

Author(s):

Sebastian Nowak ◽

Narine Mesropyan ◽

Anton Faron ◽

Wolfgang Block ◽

Martin Reuter ◽

...

Keyword(s):

Neural Networks ◽

Liver Cirrhosis ◽

Transfer Learning ◽

Convolutional Neural Networks ◽

Classification Accuracy ◽

Automatic Segmentation ◽

Analysis Data ◽

Caudate Lobe ◽

Natural Image ◽

Liver Segmentation

Abstract Objectives To investigate the diagnostic performance of deep transfer learning (DTL) to detect liver cirrhosis from clinical MRI. Methods The dataset for this retrospective analysis consisted of 713 (343 female) patients who underwent liver MRI between 2017 and 2019. In total, 553 of these subjects had a confirmed diagnosis of liver cirrhosis, while the remainder had no history of liver disease. T2-weighted MRI slices at the level of the caudate lobe were manually exported for DTL analysis. Data were randomly split into training, validation, and test sets (70%/15%/15%). A ResNet50 convolutional neural network (CNN) pre-trained on the ImageNet archive was used for cirrhosis detection with and without upstream liver segmentation. Classification performance for detection of liver cirrhosis was compared to two radiologists with different levels of experience (4th-year resident, board-certified radiologist). Segmentation was performed using a U-Net architecture built on a pre-trained ResNet34 encoder. Differences in classification accuracy were assessed by the χ2-test. Results Dice coefficients for automatic segmentation were above 0.98 for both validation and test data. The classification accuracy of liver cirrhosis on validation (vACC) and test (tACC) data for the DTL pipeline with upstream liver segmentation (vACC = 0.99, tACC = 0.96) was significantly higher compared to the resident (vACC = 0.88, p < 0.01; tACC = 0.91, p = 0.01) and to the board-certified radiologist (vACC = 0.96, p < 0.01; tACC = 0.90, p < 0.01). Conclusion This proof-of-principle study demonstrates the potential of DTL for detecting cirrhosis based on standard T2-weighted MRI. The presented method for image-based diagnosis of liver cirrhosis demonstrated expert-level classification accuracy. Key Points • A pipeline consisting of two convolutional neural networks (CNNs) pre-trained on an extensive natural image database (ImageNet archive) enables detection of liver cirrhosis on standard T2-weighted MRI. • High classification accuracy can be achieved even without altering the pre-trained parameters of the convolutional neural networks. • Other abdominal structures apart from the liver were relevant for detection when the network was trained on unsegmented images.

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Automatic segmentation of multiple lesions in ultrasound breast image

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.7.10919 ◽

2018 ◽

Vol 7 (2.7) ◽

pp. 665

Author(s):

Chelladurai R ◽

Selvakumar R ◽

S Poonguzhali

Keyword(s):

Breast Lesion ◽

Automatic Segmentation ◽

Initial Step ◽

Ultra Sound ◽

Ultrasound Images ◽

Seed Point ◽

Manual Intervention ◽

Contour Shape ◽

Breast Image ◽

Trained Neural Network

Breast cancer is one of the leading cancer that affects woman all around the world. Nowadays ultra sound imaging technique is used to diagnose various cancer because of its non-ionizing, on-invasive, and cheap cost. Breast lesion region in ultrasound images are classified depending upon the contour, shape, size and textural features of the segmented region. Seed point is the initial step in segmentation of lesion regions and if that point is located outside the lesion region, it leads to wrong segmentation which results in misclassification of the lesion regions. To avoid this, most of the time the seed point is located manually. In order to avoid this manual intervention, we are proposing a novel method in locating the seed point and also segmenting the breast lesion region automatically. In this method, the image is processed with tan function for effective distinguishing of breast lesion and normal region. Then using the trained neural network, the seed point is automatically located inside the lesion region and from the seed point the region of the lesion is grown and segmented automatically. Most of the past works on automatic segmentation of lesion had concentrated only in single lesion region, but using this proposed method, we were able to automatically segment multiple lesion regions in the image. Outcome of the proposed method is to detect automatically and dynamically separate the lesion region in the range between 90% to 97.5% of images.

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Liver Segmentation in CT Images for Intervention Using a Graph-Cut Based Model

Lecture Notes in Computer Science - Abdominal Imaging. Computational and Clinical Applications ◽

10.1007/978-3-642-28557-8_20 ◽

2012 ◽

pp. 157-164 ◽

Cited By ~ 4

Author(s):

Yufei Chen ◽

Weidong Zhao ◽

Qidi Wu ◽

Zhicheng Wang ◽

Jinyong Hu

Keyword(s):

Ct Images ◽

Graph Cut ◽

Liver Segmentation

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Investigating the Relevance of Graph Cut Parameter on Interactive and Automatic Cell Segmentation

Computational and Mathematical Methods in Medicine ◽

10.1155/2018/7396910 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 1

Author(s):

Kazeem Oyeyemi Oyebode ◽

Shengzhi Du ◽

Barend Jacobus van Wyk ◽

Karim Djouani

Keyword(s):

Statistical Analysis ◽

Significant Role ◽

Energy Function ◽

Automatic Segmentation ◽

Graph Cut ◽

Cell Segmentation ◽

Medical Field ◽

Segmentation Strategy ◽

Segmentation Accuracy ◽

Graph Cut Segmentation

Graph cut segmentation provides a platform to analyze images through a global segmentation strategy, and as a result of this, it has gained a wider acceptability in many interactive and automatic segmentation fields of application, such as the medical field. The graph cut energy function has a parameter that is tuned to ensure that the output is neither oversegmented (shrink bias) nor undersegmented. Models have been proposed in literature towards the improvement of graph cut segmentation, in the context of interactive and automatic cell segmentation. Along this line of research, the graph cut parameter has been leveraged, while in some instances, it has been ignored. Therefore, in this work, the relevance of graph cut parameter on both interactive and automatic cell segmentation is investigated. Statistical analysis, based on F1 score, of three publicly available datasets of cells, suggests that the graph cut parameter plays a significant role in improving the segmentation accuracy of the interactive graph cut than the automatic graph cut.

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Swarm Intelligence Integrated Graph-Cut for Liver Segmentation from 3D-CT Volumes

The Scientific World JOURNAL ◽

10.1155/2015/823541 ◽

2015 ◽

Vol 2015 ◽

pp. 1-12 ◽

Cited By ~ 2

Author(s):

Maya Eapen ◽

Reeba Korah ◽

G. Geetha

Keyword(s):

Swarm Intelligence ◽

Liver Volume ◽

Graph Cut ◽

Experimental Result ◽

Liver Segmentation ◽

Large Variability ◽

Contrast Enhanced ◽

Adaptive Weight ◽

Efficiency And Effectiveness ◽

Information Intensity

The segmentation of organs in CT volumes is a prerequisite for diagnosis and treatment planning. In this paper, we focus on liver segmentation from contrast-enhanced abdominal CT volumes, a challenging task due to intensity overlapping, blurred edges, large variability in liver shape, and complex background with cluttered features. The algorithm integrates multidiscriminative cues (i.e., prior domain information, intensity model, and regional characteristics of liver in a graph-cut image segmentation framework). The paper proposes a swarm intelligence inspired edge-adaptive weight function for regulating the energy minimization of the traditional graph-cut model. The model is validated both qualitatively (by clinicians and radiologists) and quantitatively on publically available computed tomography (CT) datasets (MICCAI 2007 liver segmentation challenge, 3D-IRCAD). Quantitative evaluation of segmentation results is performed using liver volume calculations and a mean score of 80.8% and 82.5% on MICCAI and IRCAD dataset, respectively, is obtained. The experimental result illustrates the efficiency and effectiveness of the proposed method.

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