| 1. |
- Andersson, Thord, et al.
(författare)
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A Fast Optimization Method for Level Set Segmentation
- 2009
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Ingår i: Image Analysis. - Berlin, Heidelberg : Springer Berlin/Heidelberg. - 9783642022296 - 9783642022302 ; , s. 400-409
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Konferensbidrag (refereegranskat)abstract
- Level set methods are a popular way to solve the image segmentation problem in computer image analysis. A contour is implicitly represented by the zero level of a signed distance function, and evolved according to a motion equation in order to minimize a cost function. This function defines the objective of the segmentation problem and also includes regularization constraints. Gradient descent search is the de facto method used to solve this optimization problem. Basic gradient descent methods, however, are sensitive for local optima and often display slow convergence. Traditionally, the cost functions have been modified to avoid these problems. In this work, we instead propose using a modified gradient descent search based on resilient propagation (Rprop), a method commonly used in the machine learning community. Our results show faster convergence and less sensitivity to local optima, compared to traditional gradient descent.
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| 2. |
- Andersson, Thord, et al.
(författare)
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Consistent intensity inhomogeneity correction in water-fat MRI
- 2015
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Ingår i: Journal of Magnetic Resonance Imaging. - : Wiley-Blackwell. - 1053-1807 .- 1522-2586. ; 42:2
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Tidskriftsartikel (refereegranskat)abstract
- PURPOSE: To quantitatively and qualitatively evaluate the water-signal performance of the consistent intensity inhomogeneity correction (CIIC) method to correct for intensity inhomogeneitiesMETHODS: Water-fat volumes were acquired using 1.5 Tesla (T) and 3.0T symmetrically sampled 2-point Dixon three-dimensional MRI. Two datasets: (i) 10 muscle tissue regions of interest (ROIs) from 10 subjects acquired with both 1.5T and 3.0T whole-body MRI. (ii) Seven liver tissue ROIs from 36 patients imaged using 1.5T MRI at six time points after Gd-EOB-DTPA injection. The performance of CIIC was evaluated quantitatively by analyzing its impact on the dispersion and bias of the water image ROI intensities, and qualitatively using side-by-side image comparisons.RESULTS: CIIC significantly ( P1.5T≤2.3×10-4,P3.0T≤1.0×10-6) decreased the nonphysiological intensity variance while preserving the average intensity levels. The side-by-side comparisons showed improved intensity consistency ( Pint≤10-6) while not introducing artifacts ( Part=0.024) nor changed appearances ( Papp≤10-6).CONCLUSION: CIIC improves the spatiotemporal intensity consistency in regions of a homogenous tissue type.
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| 3. |
- Andersson, Thord, 1972-, et al.
(författare)
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Geodesic registration for interactive atlas-based segmentation using learned multi-scale anatomical manifolds
- 2018
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Ingår i: Pattern Recognition Letters. - : Elsevier. - 0167-8655 .- 1872-7344. ; 112, s. 340-345
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Tidskriftsartikel (refereegranskat)abstract
- Atlas-based segmentation is often used to segment medical image regions. For intensity-normalized data, the quality of these segmentations is highly dependent on the similarity between the atlas and the target under the used registration method. We propose a geodesic registration method for interactive atlas-based segmentation using empirical multi-scale anatomical manifolds. The method utilizes unlabeled images together with the labeled atlases to learn empirical anatomical manifolds. These manifolds are defined on distinct scales and regions and are used to propagate the labeling information from the atlases to the target along anatomical geodesics. The resulting competing segmentations from the different manifolds are then ranked according to an image-based similarity measure. We used image volumes acquired using magnetic resonance imaging from 36 subjects. The performance of the method was evaluated using a liver segmentation task. The result was then compared to the corresponding performance of direct segmentation using Dice Index statistics. The method shows a significant improvement in liver segmentation performance between the proposed method and direct segmentation. Furthermore, the standard deviation in performance decreased significantly. Using competing complementary manifolds defined over a hierarchy of region of interests gives an additional improvement in segmentation performance compared to the single manifold segmentation.
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| 4. |
- Andersson, Thord, et al.
(författare)
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Modified Gradient Search for Level Set Based Image Segmentation
- 2013
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Ingår i: IEEE Transactions on Image Processing. - : IEEE Signal Processing Society. - 1057-7149 .- 1941-0042. ; 22:2, s. 621-630
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Tidskriftsartikel (refereegranskat)abstract
- Level set methods are a popular way to solve the image segmentation problem. The solution contour is found by solving an optimization problem where a cost functional is minimized. Gradient descent methods are often used to solve this optimization problem since they are very easy to implement and applicable to general nonconvex functionals. They are, however, sensitive to local minima and often display slow convergence. Traditionally, cost functionals have been modified to avoid these problems. In this paper, we instead propose using two modified gradient descent methods, one using a momentum term and one based on resilient propagation. These methods are commonly used in the machine learning community. In a series of 2-D/3-D-experiments using real and synthetic data with ground truth, the modifications are shown to reduce the sensitivity for local optima and to increase the convergence rate. The parameter sensitivity is also investigated. The proposed methods are very simple modifications of the basic method, and are directly compatible with any type of level set implementation. Downloadable reference code with examples is available online.
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| 5. |
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| 6. |
- Borga, Magnus, et al.
(författare)
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Semi-Supervised Learning of Anatomical Manifolds for Atlas-Based Segmentation of Medical Images
- 2016
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Ingår i: Proceedings of the 23rd International Conference on Pattern Recognition (ICPR). - : IEEE Computer Society. - 9781509048472 - 9781509048489 ; , s. 3146-3149
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Konferensbidrag (refereegranskat)abstract
- This paper presents a novel method for atlas-based segmentation of medical images. The method uses semi- supervised learning of a graph describing a manifold of anatom- ical variations of whole-body images, where unlabelled data are used to find a path with small deformations from the labelled atlas to the target image. The method is evaluated on 36 whole-body magnetic resonance images with manually segmented livers as ground truth. Significant improvement (p < 0.001) was obtained compared to direct atlas-based registration.
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| 7. |
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| 8. |
- Läthén, Gunnar, et al.
(författare)
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Momentum Based Optimization Methods for Level Set Segmentation
- 2009
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Ingår i: Momentum Based Optimization Methods for Level Set Segmentation. - Berlin : Springer Berlin/Heidelberg. - 3642022553 - 9783642022555 - 9783642022562 ; , s. 124-136
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Konferensbidrag (refereegranskat)abstract
- Segmentation of images is often posed as a variational problem. As such, it is solved by formulating an energy functional depending on a contour and other image derived terms. The solution of the segmentation problem is the contour which extremizes this functional. The standard way of solving this optimization problem is by gradient descent search in the solution space, which typically suffers from many unwanted local optima and poor convergence. Classically, these problems have been circumvented by modifying the energy functional. In contrast, the focus of this paper is on alternative methods for optimization. Inspired by ideas from the machine learning community, we propose segmentation based on gradient descent with momentum. Our results show that typical models hampered by local optima solutions can be further improved by this approach. We illustrate the performance improvements using the level set framework.
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