3D segmentation of rodent brain structures using Active Volume Model with shape priors

Zhang, Shaoting; Huang, Junzhou; Uzunbaş, Mustafa Gökhan; Shen, Tian; Delis, Foteini; Huang, Xiaolei; Volkow, Nora D.; Thanos, Panayotis K.; Metaxas, Dimitris N.

doi:10.1109/isbi.2011.5872439

Cited by 5 publications

(3 citation statements)

References 12 publications

(12 reference statements)

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“…Note that all deformation modules are the same and based on both gradient and region information. We compared the methods of combing the robust deformation model with a traditional smoothness shape prior [10], an independent shape prior [12] for each structure and hierarchical shape priors (i.e., the proposed method). The same parameters are used in all deformation modules.…”

Section: Methodsmentioning

confidence: 99%

“…However, these methods may need a large amount of 3D training data, whose creation and maintenance can be difficult and time consuming in practice. Combining AVM and ASM is able to handle both complex texture and shape details without a large set of 3D training data [12]. However, this approach still cannot handle multiple structures simultaneously because, with multiple structures together, there is a larger variability and thus it usually requires more training samples in order to capture such variability.…”

Section: Introductionmentioning

confidence: 99%

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3D Segmentation of Rodent Brain Structures Using Hierarchical Shape Priors and Deformable Models

Zhang

Huang

Uzunbaş

et al. 2011

Lecture Notes in Computer Science

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Abstract. In this paper, we propose a method to segment multiple rodent brain structures simultaneously. This method combines deformable models and hierarchical shape priors within one framework. The deformation module employs both gradient and appearance information to generate image forces to deform the shape. The shape prior module uses Principal Component Analysis to hierarchically model the multiple structures at both global and local levels. At the global level, the statistics of relative positions among different structures are modeled. At the local level, the shape statistics within each structure is learned from training samples. Our segmentation method adaptively employs both priors to constrain the intermediate deformation result. This prior constraint improves the robustness of the model and benefits the segmentation accuracy. Another merit of our prior module is that the size of the training data can be small, because the shape prior module models each structure individually and combines them using global statistics. This scheme can preserve shape details better than directly applying PCA on all structures. We use this method to segment rodent brain structures, such as the cerebellum, the left and right striatum, and the left and right hippocampus. The experiments show that our method works effectively and this hierarchical prior improves the segmentation performance.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

3D Segmentation of Rodent Brain Structures Using Hierarchical Shape Priors and Deformable Models

Zhang

Huang

Uzunbaş

et al. 2011

Lecture Notes in Computer Science

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“…Zhang et al [ 20 ] proposed that object boundary definition is an important task in brain image analysis. The brain structure can be used to improve the disease detection rate at earlier stages.…”

Section: Introductionmentioning

confidence: 99%

Ischemic Stroke Detection System with a Computer-Aided Diagnostic Ability Using an Unsupervised Feature Perception Enhancement Method

Tyan

Chin

et al. 2014

International Journal of Biomedical Imaging

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We propose an ischemic stroke detection system with a computer-aided diagnostic ability using a four-step unsupervised feature perception enhancement method. In the first step, known as preprocessing, we use a cubic curve contrast enhancement method to enhance image contrast. In the second step, we use a series of methods to extract the brain tissue image area identified during preprocessing. To detect abnormal regions in the brain images, we propose using an unsupervised region growing algorithm to segment the brain tissue area. The brain is centered on a horizontal line and the white matter of the brain's inner ring is split into eight regions. In the third step, we use a coinciding regional location method to find the hybrid area of locations where a stroke may have occurred in each cerebral hemisphere. Finally, we make corrections and mark the stroke area with red color. In the experiment, we tested the system on 90 computed tomography (CT) images from 26 patients, and, with the assistance of two radiologists, we proved that our proposed system has computer-aided diagnostic capabilities. Our results show an increased stroke diagnosis sensitivity of 83% in comparison to 31% when radiologists use conventional diagnostic images.

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Automatic Brain Extraction for Rodent MRI Images

Liu

Unsal

et al. 2020

Neuroinform

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3D segmentation of rodent brain structures using Active Volume Model with shape priors

Cited by 5 publications

References 12 publications

3D Segmentation of Rodent Brain Structures Using Hierarchical Shape Priors and Deformable Models

3D Segmentation of Rodent Brain Structures Using Hierarchical Shape Priors and Deformable Models

Ischemic Stroke Detection System with a Computer-Aided Diagnostic Ability Using an Unsupervised Feature Perception Enhancement Method

Automatic Brain Extraction for Rodent MRI Images

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