| 1. |
- Banerjee, Subhashis, et al.
(författare)
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Topology-Aware Learning for Volumetric Cerebrovascular Segmentation
- 2022
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Ingår i: 2022 IEEE International Symposium on Biomedical Imaging (IEEE ISBI 2022). - : IEEE. - 9781665429238 ; , s. 1-4
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Konferensbidrag (refereegranskat)abstract
- This paper presents a topology-aware learning strategy for volumetric segmentation of intracranial cerebrovascular structures. We propose a multi-task deep CNN along with a topology-aware loss function for this purpose. Along with the main task (i.e. segmentation), we train the model to learn two related auxiliary tasks viz. learning the distance transform for the voxels on the surface of the vascular tree and learning the vessel centerline. This provides additional regularization and allows the encoder to learn higher-level intermediate representations to boost the performance of the main task. We compare the proposed method with six state-of-the-art deep learning-based 3D vessel segmentation methods, by using a public Time-Of-Flight Magnetic Resonance Angiography (TOF-MRA) dataset. Experimental results demonstrate that the proposed method has the best performance in this particular context.
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| 2. |
- Gonzales, Ricardo A., et al.
(författare)
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Automated Measurements of Mitral and Tricuspid Annular Dimensions in Cardiovascular Magnetic Resonance
- 2022
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Ingår i: ISBI 2022 - Proceedings : 2022 IEEE International Symposium on Biomedical Imaging - 2022 IEEE International Symposium on Biomedical Imaging. - 1945-7928 .- 1945-8452. - 9781665429238 ; 2022-March
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Konferensbidrag (refereegranskat)abstract
- Our recent work on mitral and tricuspid valve tracking in cardiovascular magnetic resonance (CMR) imaging to obtain accurate evaluations of longitudinal myocardial valve motion (both relaxation and contraction) has enabled an automated diastolic function assessment (e') with CMR. Its time-resolved capability allows a further evaluation of the valve dynamics by providing valve dimension measurements, which are essential to define the etiologies and mechanisms of valve regurgitation. In this paper, we extended the framework to automatically measure mitral annular (MA) and tricuspid annular (TA) dimensions in CMR long-axis cines with a residual neural network backbone. The framework is able to measure MA and TA diameters with an overall excellent accuracy (mean ICC=0.92), on par with an evaluated inter-observer variability (mean ICC=0.92), and to distinguish valvular dimensions between healthy controls and patients with chronic heart failure (p<0.001). Dimension measurements may benefit patients requiring annular sizing and planning of valvular interventions.
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| 3. |
- Miri, Maliheh, et al.
(författare)
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Enhanced Motor Imagery-Based Eeg Classification Using A Discriminative Graph Fourier Subspace
- 2022
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Ingår i: ISBI 2022 - Proceedings : 2022 IEEE International Symposium on Biomedical Imaging - 2022 IEEE International Symposium on Biomedical Imaging. - 1945-7928 .- 1945-8452. - 9781665429238 - 9781665429245 ; 2022-March
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Konferensbidrag (refereegranskat)abstract
- Dealing with irregular domains, graph signal processing (GSP) has attracted much attention especially in brain imaging analysis. Motor imagery tasks are extensively utilized in brain-computer interface (BCI) systems that perform classification using features extracted from Electroencephalogram signals. In this paper, a GSP-based approach is presented for two-class motor imagery tasks classification. The proposed method exploits simultaneous diagonalization of two matrices that quantify the covariance structure of graph spectral representation of data from each class, providing a discriminative subspace where distinctive features are extracted from the data. The performance of the proposed method was evaluated on Dataset IVa from BCI Competition III. Experimental results show that the proposed method outperforms two state-of-the-art alternative methods.
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