Segmentation of Neck Lymph Nodes in CT Datasets with Stable 3D Mass-Spring Models

Dornheim, Jana; Seim, Heiko; Preim, Bernhard; Hertel, Ilka; Strauß, Gero

doi:10.1007/11866763_111

Cited by 22 publications

(5 citation statements)

References 6 publications

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“…Our approach is different from the approaches presented in Refs. 11,15,26,42 in which these approaches used multi images to reconstruct the 3D shape of the blood vessel while our approach proposes using a single frame. In addition, implementing the proposed algorithm using computer aided systems can reduce the cost and time of the processing because the resilience of these systems.…”

Section: Discussionmentioning

confidence: 99%

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3D-shape formation of blood vessels based on computer aided design system

Abdul-Ameer

2023

Advances in Mechanical Engineering

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This paper proposes and tests a computerized approach for constructing a 3D model of blood vessels from angiogram images. The approach is divided into two steps, image features extraction and solid model formation. In the first step, image morphological operations and post-processing techniques are used for extracting geometrical entities from the angiogram image. These entities are the middle curve and outer edges of the blood vessel, which are then passed to a computer-aided graphical system for the second phase of processing. The system has embedded programming capabilities and pre-programmed libraries for automating a sequence of events that are exploited to create a solid model of the blood vessel. The gradient of the middle curve is adopted to steer the vessel’s direction, while the cross-sections of the blood vessel are formed as a sequence of circles lying in planes that are orthogonal to the gradients of the middle curves. The radii for the circles are estimated as a distance between the intersection points of the blood vessel edges with the orthogonal plane to the middle curve gradient. The system then uses these circles and the middle curve gradients to produce a solid volume that represents the 3D shape of the blood vessel. The method was tested and evaluated using different cases of angiogram images, and showed a reasonable agreement between the generated shapes and the tested images.

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Section: Discussionmentioning

confidence: 99%

“…2,9,10 Generating the blood vessel's centerline and determining each bifurcation point along the vessel tree is challenging. [9][10][11][12] Here, we propose solutions with the assistance of computer aided design. Below, we will illustrate the most relevant works.…”

Section: Introductionmentioning

confidence: 99%

3D-shape formation of blood vessels based on computer aided design system

Abdul-Ameer

2023

Advances in Mechanical Engineering

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“…Deformable model first initializes a mesh representing the target object close to the desired location and orientation [13,1,2]. The boundary and regional information are then used to evolve the mesh vertices along their normal directions to adhere to the image content.…”

Section: Shape Priors With a Mesh-based Representationmentioning

confidence: 99%

“…The boundary and regional information are then used to evolve the mesh vertices along their normal directions to adhere to the image content. An elastic force is also often used to limit the amount of warp to enforce the shape prior [13,2]. Such evolution is repeated multiple times until convergence.…”

Section: Shape Priors With a Mesh-based Representationmentioning

confidence: 99%

Optimal multi-object segmentation with novel gradient vector flow based shape priors

Bai

Shah

2018

Computerized Medical Imaging and Graphics

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Shape priors have been widely utilized in medical image segmentation to improve segmentation accuracy and robustness. A major way to encode such a prior shape model is to use a mesh representation, which is prone to causing self-intersection or mesh folding. Those problems require complex and expensive algorithms to mitigate. In this paper, we propose a novel shape prior directly embedded in the voxel grid space, based on gradient vector flows of a pre-segmentation. The flexible and powerful prior shape representation is ready to be extended to simultaneously segmenting multiple interacting objects with minimum separation distance constraint. The segmentation problem of multiple interacting objects with shape priors is formulated as a Markov Random Field problem, which seeks to optimize the label assignment (objects or background) for each voxel while keeping the label consistency between the neighboring voxels. The optimization problem can be efficiently solved with a single minimum s-t cut in an appropriately constructed graph. The proposed algorithm has been validated on two multi-object segmentation applications: the brain tissue segmentation in MRI images and the bladder/prostate segmentation in CT images. Both sets of experiments showed superior or competitive performance of the proposed method to the compared state-of-the-art methods.

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“…In [33], a novel approach to segment thoracoabdominal LN clusters combined holistically-nested neural networks and structured optimization. Other works addressed the segmentation of LNs in head and neck CT images [34,35,36]. Recently, deep learning techniques have been developed to segment LNs.…”

Section: Introductionmentioning

confidence: 99%

Computer-aided Lymph Node Detection using Pelvic Magnetic Resonance Imaging

Bnouni

Rekik

Rhim

et al. 2020

IJCDS

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Pelvic Lymph Nodes (PLNs) segmentation and classification are fundamental tools in the medical image analysis of pelvic gynecological cancer such as endometrial and cervical cancer. Often used by the radiologist, PLN classification requires detailed knowledge of the morphological features of PLNs, derived from size, shape, contour and heterogeneous appearance. Accurate PLN segmentation is an essential step in PLN classification. In order to supply the best assessment of a nodal status, semiautomatic and automatic PLN segmentation and classification methods are highly desired as they can strongly capture the wide variability in morphological features and reduce classification errors due to the inter and intra-observer variability, while avoiding the time-consuming for manual delineation of PLN boundary. Nevertheless, semi-automatic segmentation methods require the clinician intervention to select the initial seed point. However, typical semi-automatic PLN segmentation methods might fail due to (1) the intensity inhomogeneity, noise and low contrast in medical images, and (2) the position of the starting point. Thus, the performance of these methods can be enhanced by using a preprocessing-based iterative segmentation approach. Currently, Magnetic Resonance Imaging (MRI) is the most common imaging modality used for staging endometrial and cervical cancer, evaluation of PLN involvement and selection of therapeutic strategy. PLN detection using classic features can be challenging due to the similarity between normal and abnormal PLNs structures. In pelvic cancer and metastatic PLN, Diffusion Weighted (DW)-MRI exhibits brighter areas indicating tumor and metastatic PLN. This paper combines anatomic T2-Weighted (T2-w) imaging with DW imaging. Specifically, we propose a computer-aided pelvic framework, which leverages (1) an ensemble preprocessing method to improve PLN segmentation, (2) the iterative correction of the position of the initial point by executing the segmentation algorithm several times in succession, (3) the fusion of structural and diffusion MRI and, (4) the extraction of morphological features of segmented PLNs (axial T2-w image) as well as intensity feature derived from the fused image for the final classification of PLNs as suspect or non-suspect. Research in the field of PLN detection is important as it can help doctors to better detect cervical and endometrial cancer and decide the appropriate treatment. To the best of our knowledge, this is the first work to segment and classify PLN. Our preprocessing-based iterative segmentation approach significantly (p<0.05) improved comparison segmentation methods, with a segmentation accuracy boosted from 61.37% for the conventional region-growing algorithm to 66.53% for the proposed method. Furthermore, we obtained an average accuracy of 78.50% for pelvic nodule classification.

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Segmentation of Neck Lymph Nodes in CT Datasets with Stable 3D Mass-Spring Models

Cited by 22 publications

References 6 publications

3D-shape formation of blood vessels based on computer aided design system

3D-shape formation of blood vessels based on computer aided design system

Optimal multi-object segmentation with novel gradient vector flow based shape priors

Computer-aided Lymph Node Detection using Pelvic Magnetic Resonance Imaging

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