| 1. |
- Källén, Hanna, et al.
(författare)
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Towards Grading Gleason Score using Generically Trained Deep convolutional Neural Networks
- 2016
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Ingår i: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781479923496 - 9781479923502 ; 2016-June, s. 1163-1167
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Konferensbidrag (refereegranskat)abstract
- We developed an automatic algorithm with the purpose to assist pathologists to report Gleason score on malignant prostatic adenocarcinoma specimen. In order to detect and classify the cancerous tissue, a deep convolutional neural network that had been pre-trained on a large set of photographic images was used. A specific aim was to support intuitive interaction with the result, to let pathologists adjust and correct the output. Therefore, we have designed an algorithm that makes a spatial classification of the whole slide into the same growth patterns as pathologists do. The 22-layer network was cut at an earlier layer and the output from that layer was used to train both a random forest classifier and a support vector machines classifier. At a specific layer a small patch of the image was used to calculate a feature vector and an image is represented by a number of those vectors. We have classified both the individual patches and the entire images. The classification results were compared for different scales of the images and feature vectors from two different layers from the network. Testing was made on a dataset consisting of 213 images, all containing a single class, benign tissue or Gleason score 3-5. Using 10-fold cross validation the accuracy per patch was 81 %. For whole images, the accuracy was increased to 89 %.
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| 2. |
- Abramian, David, 1992-, et al.
(författare)
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Improved Functional MRI Activation Mapping in White Matter Through Diffusion-Adapted Spatial Filtering
- 2020
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Ingår i: ISBI 2020. - : IEEE. - 1945-8452 .- 1945-7928. - 9781538693308
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Konferensbidrag (refereegranskat)abstract
- Brain activation mapping using functional MRI (fMRI) based on blood oxygenation level-dependent (BOLD) contrast has been conventionally focused on probing gray matter, the BOLD contrast in white matter having been generally disregarded. Recent results have provided evidence of the functional significance of the white matter BOLD signal, showing at the same time that its correlation structure is highly anisotropic, and related to the diffusion tensor in shape and orientation. This evidence suggests that conventional isotropic Gaussian filters are inadequate for denoising white matter fMRI data, since they are incapable of adapting to the complex anisotropic domain of white matter axonal connections. In this paper we explore a graph-based description of the white matter developed from diffusion MRI data, which is capable of encoding the anisotropy of the domain. Based on this representation we design localized spatial filters that adapt to white matter structure by leveraging graph signal processing principles. The performance of the proposed filtering technique is evaluated on semi-synthetic data, where it shows potential for greater sensitivity and specificity in white matter activation mapping, compared to isotropic filtering.
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| 3. |
- Behjat, Hamid, et al.
(författare)
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Characterization of Spatial Dynamics of Fmri Data in White Matter Using Diffusion-Informed White Matter Harmonics
- 2021
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Ingår i: 2021 IEEE 18th International Symposium On Biomedical Imaging (ISBI). - : Institute of Electrical and Electronics Engineers (IEEE). - 1945-7928 .- 1945-8452. - 9781665412469 - 9781665429474
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Konferensbidrag (refereegranskat)abstract
- In this work, we leverage the Laplacian eigenbasis of voxelwise white matter (WM) graphs derived from diffusionweighted MRI data, dubbed WM harmonics, to characterize the spatial structure of WM fMRI data. Our motivation for such a characterization is based on studies that show WM fMRI data exhibit a spatial correlational anisotropy that coincides with underlying fiber patterns. By quantifying the energy content of WM fMRI data associated with subsets of WM harmonics across multiple spectral bands, we show that the data exhibits notable subtle spatial modulations under functional load that are not manifested during rest. WM harmonics provide a novel means to study the spatial dynamics of WM fMRI data, in such way that the analysis is informed by the underlying anatomical structure.
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| 4. |
- Behjat, Hamid, et al.
(författare)
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Spectral Characterization of Functional MRI Data on Voxel-Resolution Cortical Graphs
- 2020
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Ingår i: ISBI 2020 - 2020 IEEE International Symposium on Biomedical Imaging. - 1945-7928 .- 1945-8452. - 9781538693308 ; 2020-April, s. 558-562
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Konferensbidrag (refereegranskat)abstract
- The human cortical layer exhibits a convoluted morphology that is unique to each individual. Conventional volumetric fMRI processing schemes take for granted the rich information provided by the underlying anatomy. We present a method to study fMRI data on subject-specific cerebral hemisphere cortex (CHC) graphs, which encode the cortical morphology at the resolution of voxels in 3-D. Using graph signal processing principles, we study spectral energy metrics associated to fMRI data, on 100 subjects from the Human Connectome Project database, across seven tasks. Experimental results signify the strength of CHC graphs' Laplacian eigenvector bases in capturing subtle spatial patterns specific to different functional loads as well as to sets of experimental conditions within each task.
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| 5. |
- Behjat, Hamid, et al.
(författare)
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Statistical parametric mapping of functional MRI data using wavelets adapted to the cerebral cortex
- 2013
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Ingår i: [Host publication title missing]. - 1945-7928 .- 1945-8452. ; , s. 1070-1073
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Konferensbidrag (refereegranskat)abstract
- Wavelet approaches have been successfully applied to the detection of brain activity in fMRI data. Spatial activation patterns have a compact representation in the wavelet domain. However, classical wavelets designed for regular Euclidean spaces are not optimal for the topologically complicated gray-matter (GM) domain where activation is expected. We hypothesized that wavelet bases that are adapted to the structure of the GM, would be more powerful in detecting brain activity. We therefore combine (1) a GM-based graph wavelet transform as an advanced spatial transformation for fMRI data with (2) the wavelet-based statistical parametric mapping framework (WSPM). We introduce suitable design choices for the graph wavelet transform and evaluate the performance of the proposed approach both on simulated and real fMRI data. Compared to SPM and conventional WSPM, the graph-based WSPM shows improved detection of finely 3D-structured brain activity.
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| 6. |
- 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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| 7. |
- Guo, Qingqing, et al.
(författare)
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LGANet : Local-Global Augmentation Network for Skin Lesion Segmentation
- 2023
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Ingår i: 2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023. - 1945-8452 .- 1945-7928. - 9781665473583 ; 2023-April
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Konferensbidrag (refereegranskat)abstract
- Automatic segmentation of skin lesion is still challenging due to ambiguous boundary and noise interference of lesion regions. Recent exiting Transformer-based methods often directly apply Transformer to obtain long-range dependency to overcome these problems. However, they generally do not consider that patch partitioning strategy of Transformer could lead to the loss of local details around boundaries. Furthermore, dependencies across local windows only represent global information at a coarse level. Therefore, to overcome the limitations, two novel modules, Local Focus Module (LFM) and Global Augmentation Module (GAM) are proposed in this paper. LFM learns the local context around boundary regions to strengthen the discrimination between classes. And GAM learns the global context at a finer level to enhance global feature representation. Integrating LFM and GAM, a new Transformer encoder based framework, Local-Global Augmentation Network (LGANet), is proposed. LGANet is efficient in segmenting lesions with ambiguous boundary and with noise interference and its performances are demonstrated with extensive experiments on two public skin lesion segmentation datasets.
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| 8. |
- Kaliyugarasan, Satheshkumar, et al.
(författare)
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Multi-Center CNN-Based Spine Segmentation from T2W MRI Using Small Amounts of Data
- 2023
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Ingår i: Proceedings - International Symposium on Biomedical Imaging. - 1945-7928 .- 1945-8452. - 9781665473583
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Konferensbidrag (refereegranskat)abstract
- Segmentation of the spinal tissues on MRI is the basis for quantitative analyses, but time-consuming if done manually. In this work, we construct a pipeline for automatic vertebrae segmentation from T2w MRI scans, assessing performance and generalizability by external validation. Our study used 15 scans from one site (Haukeland University Hospital, HUH) and 10 scans from another (Sahlgrenska University Hospital, SUH). MRI experts manually delineated the vertebral bodies Th12-L5 on all the HUH data and a subset of six scans from SUH. We trained multiple convolutional neural networks, assessing the performance in an experimental design tailored to small-data contexts and also on external data. Our best model achieved a mean Dice score of 0.899. This is comparable to results in the literature, but our system required much less training data.
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| 9. |
- Kofler, Florian, et al.
(författare)
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Approaching Peak Ground Truth
- 2023
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Ingår i: Proceedings - International Symposium on Biomedical Imaging. - 1945-7928 .- 1945-8452. - 9781665473583
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Konferensbidrag (refereegranskat)abstract
- Machine learning models are typically evaluated by computing similarity with reference annotations and trained by maximizing similarity with such. Especially in the biomedical domain, annotations are subjective and suffer from low inter-and intra-rater reliability. Since annotations only reflect one interpretation of the real world, this can lead to sub-optimal predictions even though the model achieves high similarity scores. Here, the theoretical concept of Peak Ground Truth (PGT) is introduced. PGT marks the point beyond which an increase in similarity with the reference annotation stops translating to better Real World Model Performance (RWMP). Additionally, a quantitative technique to approximate PGT by computing inter- and intra-rater reliability is proposed. Finally, four categories of PGT-aware strategies to evaluate and improve model performance are reviewed.
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| 10. |
- 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. - 1945-8452 .- 1945-7928. - 9781665429245 - 9781665429238 ; 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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| 11. |
- Yaroshenko, A., et al.
(författare)
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Preclinical x-ray dark-field radiography for pulmonary emphysema evaluation
- 2013
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Ingår i: ISBI 2013 - 2013 IEEE 10th International Symposium on Biomedical Imaging : From Nano to Macro - From Nano to Macro. - 1945-7928 .- 1945-8452. - 9781467364553 - 9781467364560 ; , s. 370-373
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Konferensbidrag (refereegranskat)abstract
- Pulmonary emphysema is a widespread disorder characterized by irreversible destruction of alveolar walls. The spatial distribution of the disease, so far, could only be obtained using an x-ray CT scan, implying a high patient dose. X-ray scattering on alveolar structures is measured in the dark-field signal. The signal is dependent on the size of alveoli and therefore, a combination of absorption and dark-field signal is explored for mapping the distribution of emphysema in the lung on x-ray projection images. In this study three excised murine lungs with pulmonary emphysema and three control samples were imaged using a compact, cone-beam, small-animal x-ray dark-field scanner with a polychromatic source. Statistical analysis of the results, based on a combination of transmission and dark-field signals, revealed a distinct difference between emphysematous and control samples. Subsequently, the distribution of emphysema was mapped out per-pixel for the lungs and showed good agreement with histological findings.
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| 12. |
- Alipoor, Mohammad, 1983, et al.
(författare)
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Determinant of the information matrix: a new rotation invariant optimality metric to design gradient encoding schemes
- 2015
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Ingår i: 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015, Brooklyn, United States, 16-19 April 2015. - 1945-8452. - 9781479923748 ; 2015-July, s. 462-465
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Konferensbidrag (refereegranskat)abstract
- Minimum condition number (CN) gradient encoding schemewas introduced to diffusion MRI community more than adecade ago. It’s computation requires tedious numerical optimization which usually leads to sub-optimal solutions. TheCN does not reflect any benefits in acquiring more measurements, i.e. it’s optimal value is constant for any numberof measurements. Further, it is variable under rotation. Inthis paper we (i) propose an accurate method to computeminimum condition number scheme; and (ii) introduce determinant of the information matrix (DIM) as a new optimality metric that scales with number of measurements anddoes reflect what one would gain from acquiring more measurements. Theoretical analysis shows that DIM is rotationinvariant. Evaluations on state-of-the-art encoding schemesproves the relevance and superiority of the proposed metriccompared to condition number.
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| 13. |
- Alipoor, Mohammad, 1983, et al.
(författare)
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Icosahedral gradient encoding scheme for an arbitrary number of measurements
- 2015
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Ingår i: International symposium on biomedical imaging. - 1945-8452. - 9781479923748 ; 2015-July, s. 959-962
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Konferensbidrag (refereegranskat)abstract
- The icosahedral gradient encoding scheme (GES) is widelyused in diffusion MRI community due to its uniformly distributed orientations and rotationally invariant condition number. The major drawback with this scheme is that it is notavailable for arbitrary number of measurements. In this paper(i) we propose an algorithm to find the icosahedral schemefor any number of measurements. Performance of the obtained GES is evaluated and compared with that of Jones andtraditional icosahedral schemes in terms of condition number,standard deviation of the estimated fractional anisotropy anddistribution of diffusion sensitizing directions; and (ii) we introduce minimum eigenvalue of the information matrix as anew optimality metric to replace condition number. Unlikecondition number, it is proportional to the number of measurements and thus in agreement with the intuition that moremeasurements leads to more robust tensor estimation. Furthermore, it may independently be maximized to design GESsfor different diffusion imaging techniques.
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| 14. |
- Bajic, Buda, et al.
(författare)
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Blind restoration of images degraded with mixed poisson-Gaussian noise with application in transmission electron microscopy
- 2016
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Ingår i: 2016 Ieee 13Th International Symposium On Biomedical Imaging (ISBI). - : IEEE. - 9781479923496 - 9781479923502 ; , s. 123-127
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Konferensbidrag (refereegranskat)abstract
- Noise and blur, present in images after acquisition, negatively affect their further analysis. For image enhancement when the Point Spread Function (PSF) is unknown, blind deblurring is suitable, where both the PSF and the original image are simultaneously reconstructed. In many realistic imaging conditions, noise is modelled as a mixture of Poisson (signal-dependent) and Gaussian (signal independent) noise. In this paper we propose a blind deconvolution method for images degraded by such mixed noise. The method is based on regularized energy minimization. We evaluate its performance on synthetic images, for different blur kernels and different levels of noise, and compare with non-blind restoration. We illustrate the performance of the method on Transmission Electron Microscopy images of cilia, used in clinical practice for diagnosis of a particular type of genetic disorders.
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| 15. |
- Moreno, Rodrigo, 1973-, et al.
(författare)
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Vesselness Estimation through Higher-Order Orientation Tensors
- 2016
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Ingår i: International Symposium on Biomedical Imaging (ISBI). - : IEEE Computer Society. - 9781479923502 ; , s. 1139-1142
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Konferensbidrag (refereegranskat)abstract
- We recently proposed a method for estimating vesselness based on detection of ring patterns in the local distribution ofthe gradient. This method has a better performance than other state-of-the-art algorithms. However, the original implementation of the method makes use of the spherical harmonics transform locally, which is time consuming. In this paper we propose an equivalent formulation of the method based on higher-order tensors. A linear mapping between the spherical harmonics transform and higher-order orientation tensors is used in order to reduce the complexity of the method. With the new implementation, the analysis of computed tomography angiography data can be performed 2.6 times faster compared with the original implementation.
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| 16. |
- Suveer, Amit, et al.
(författare)
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Automated detection of cilia in low magnification transmission electron microscopy images using template matching
- 2016
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Ingår i: Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on. - : IEEE. - 9781479923496 - 9781479923502 ; , s. 386-390
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Ultrastructural analysis using Transmission Electron Microscopy (TEM) is a common approach for diagnosing primary ciliary dyskinesia. The manually performed diagnostic procedure is time consuming and subjective, and automation of the process is highly desirable. We aim at automating the search for plausible cilia instances in images at low magnification, followed by acquisition of high magnification images of regions with detected cilia for further analysis. This paper presents a template matching based method for automated detection of cilia objects in low magnification TEM images, where object radii do not exceed 10 pixels. We evaluate the performance of a series of synthetic templates generated for this purpose by comparing automated detection with results manually created by an expert pathologist. The best template achieves a detection at equal error rate of 47% which suffices to identify densely populated cilia regions suitable for high magnification imaging.
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