Learning Active Contour Models for Medical Image Segmentation

Xu, Chen; Williams, Bryan; Vallabhaneni, S. Rao; Czanner, Gabriela; Williams, Rachel; Zheng, Yalin

doi:10.1109/cvpr.2019.01190

Cited by 222 publications

(143 citation statements)

References 17 publications

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“…They are extremely useful for signal and image processing (Unser, 1999) as well as for computer animation (Maraffi, 2004) and shape reconstruction of 2D and 3D deformable objects (Song and Bai, 2008;Prasad et al, 2010). Active contours (see section 3.4.2 and Kass et al, 1988), also known as snakes, are splines governed by an energy function that introduces dynamic elements to the shape representation and are used for tasks, such as object segmentation (Marcos et al, 2018;Chen et al, 2019;Hatamizadeh et al, 2019). The advantage of splines is that compact representations of deformable objects can be built on them in accordance with the complexity of their shape at each time.…”

Section: Splinesmentioning

confidence: 99%

Modeling of Deformable Objects for Robotic Manipulation: A Tutorial and Review

et al. 2020

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Manipulation of deformable objects has given rise to an important set of open problems in the field of robotics. Application areas include robotic surgery, household robotics, manufacturing, logistics, and agriculture, to name a few. Related research problems span modeling and estimation of an object's shape, estimation of an object's material properties, such as elasticity and plasticity, object tracking and state estimation during manipulation, and manipulation planning and control. In this survey article, we start by providing a tutorial on foundational aspects of models of shape and shape dynamics. We then use this as the basis for a review of existing work on learning and estimation of these models and on motion planning and control to achieve desired deformations. We also discuss potential future lines of work.

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Section: Splinesmentioning

confidence: 99%

Modeling of Deformable Objects for Robotic Manipulation: A Tutorial and Review

et al. 2020

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“…This new loss function combines geometrical information with region similarity hence it provides more precise segmentation. The ACM loss function is used as a loss function in many deep learning models as in [42][43][44][45] .…”

Section: Deep Learning Approachesmentioning

confidence: 99%

COVID-19 Lesion Segmentation using Lung CT Scan Images: Comparative Study based on Active Contour Models

Akbari

Hassen

Al‐Maadeed

et al. 2020

Preprint

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Abstract Pneumonia is a lung infection threaten that threats all age groups. In this paper, using CT scans images, we used active contour models to evaluate and determine pneumonia infection caused by the Coronavirus disease (COVID-19). A background of active contour models (ACM) including contour representation and object boundary description methods is presented. The focus of this paper is on the conducted works based on the external forces. These methods include edge-based and region-based methods. Furthermore, the explanations of these methods, as well as the advantages and disadvantages of each method are presented. Finally, a comparison between the performances of the conducted works has been done based on a database of Lung CT Scan Images. The present review helps readers identify research starting points in active contour models on COVID19 research, which is a high priority topic to guide researchers and practitioners. In addition, when there are not enough images to use machine learning techniques, such as deep learning methods, the experimental results indicate that active contour methods obtain promising results.

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“…Since GAF can only extract statistical features from nodal time series, it is used only in community classification. All aforementioned feature-extraction methods generate matrices as features, which can be thus viewed as "grayscale-image" input data by [23][24][25], widely used in bio-medical image classification [26,27]. "Classification accuracy" in the following tables and figures is defined as the average (over multiple independent tests) of the ratios of the number of correctly classified features over the total number of features in the testing dataset.…”

Section: Numerical Testsmentioning

confidence: 99%

“…Landmark points are identified to concisely describe the geometry of the possibly massive point-cloud of features, and a novel online classification scheme is brought forth, coined online geodesic classification by tangent spaces (onlineGCT). This work highlights also the numerical tests of onlineGCT against the popular DL models GoogLeNet [23], DenseNet [24] and ResNet [25], widely used in bio-medical image classification [26,27]. Models [23][24][25] do not suffer from the previously mentioned issues of [16][17][18][19] with regards to state classification, and are adapted to fit the current setting of multilayer network data.…”

Section: Introductionmentioning

confidence: 99%

Online Classification of Dynamic Multilayer-Network Time Series in Riemannian Manifolds

Ye¹,

Slavakis²,

Nakuci³

et al. 2020

Preprint

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<div>This work exploits Riemannian manifolds to introduce a geometric framework for online state and community classification in dynamic multilayer networks where nodes are annotated with time series. A bottom-up approach is followed, starting from the extraction of Riemannian features from nodal time series, and reaching up to online/sequential classification of features via geodesic distances and angular information in the tangent spaces of a Riemannian manifold. As a case study, features in the Grassmann manifold are generated by fitting a kernel autoregressive-moving-average model to the nodal time series of the multilayer network. The paper highlights also numerical tests on synthetic and real brain-network data, where it is shown that the proposed geometric framework outperforms state-of-the-art deep-learning models in classification accuracy, especially in cases where the number of training data is small with respect to the number of the testing ones.</div><div><br></div><div>-------</div><div><br></div><div>© 20XX IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.<br></div><div><br></div>

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Learning Active Contour Models for Medical Image Segmentation

Cited by 222 publications

References 17 publications

Modeling of Deformable Objects for Robotic Manipulation: A Tutorial and Review

Modeling of Deformable Objects for Robotic Manipulation: A Tutorial and Review

COVID-19 Lesion Segmentation using Lung CT Scan Images: Comparative Study based on Active Contour Models

Online Classification of Dynamic Multilayer-Network Time Series in Riemannian Manifolds

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