Arvid Lundervold, BSc, MD, PhDProfessor of Medical Information Technology and Physiology, University of Bergen, Norway - https://www.uib.no/en/persons/Arvid.Lundervold- https://scholar.google.com/citations?user=HqmyBUUAAAAJ
Computer-aided diagnosis of Alzheimer's disease (AD) is a rapidly developing field of neuroim... more Computer-aided diagnosis of Alzheimer's disease (AD) is a rapidly developing field of neuroimaging with strong potential to be used in practice. In this context, assessment of models' robustness to noise and imaging protocol differences together with post-processing and tuning strategies are key tasks to be addressed in order to move towards successful clinical applications. In this study, we investigated the efficacy of Random Forest classifiers trained using different structural MRI measures, with and without neuroanatomical constraints in the detection and prediction of AD in terms of accuracy and between-cohort robustness. From The ADNI database, 185 AD, and 225 healthy controls (HC) were randomly split into training and testing datasets. 165 subjects with mild cognitive impairment (MCI) were distributed according to the month of conversion to dementia (4-year follow-up). Structural 1.5-T MRI-scans were processed using Freesurfer segmentation and cortical reconstruction....
Section for Medical Image Analysis and Pattern Recognition University of Bergen, Arstadveien 19, ... more Section for Medical Image Analysis and Pattern Recognition University of Bergen, Arstadveien 19, N-5009 Bergen, Norway ... In this study we demonstrate an improved differen-tiation of the most common tissue types in the human brain and surrounding structures by quantitative ...
The authors demonstrate an improved differentiation of the most common tissue types in the human ... more The authors demonstrate an improved differentiation of the most common tissue types in the human brain and surrounding structures by quantitative validation using multispectral analysis of magnetic resonance images. This is made possible by a combination of a special training technique and an increase in the number of magnetic resonance channel images with different pulse acquisition parameters. The authors give a description of the tissue-specific multivariate statistical distributions of the pixel intensity values and discuss how their properties may be explored to improve the statistical modeling further. A statistical method to estimate the tissue-specific longitudinal and transverse relaxation times is also given. It is concluded that multispectral analysis of magnetic resonance images is a valuable tool to recognize the most common normal tissue types in the brain and surrounding structures.
Background Tumor burden assessment is essential for radiation therapy (RT), treatment response ev... more Background Tumor burden assessment is essential for radiation therapy (RT), treatment response evaluation, and clinical decision-making. However, manual tumor delineation remains laborious and challenging due to radiological complexity. The objective of this study was to investigate the feasibility of the HD-GLIO tool, an ensemble of pre-trained deep learning models based on the nnUNet-algorithm, for tumor segmentation, response prediction, and its potential for clinical deployment. Methods We analyzed the predicted contrast-enhanced (CE) and non-enhancing (NE) HD-GLIO output in 49 multi-parametric MRI examinations from 23 grade-4 glioma patients. The volumes were retrospectively compared to corresponding manual delineations by 2 independent operators, before prospectively testing the feasibility of clinical deployment of HD-GLIO-output to a RT setting. Results For CE, median Dice scores were 0.81 (95% CI 0.71–0.83) and 0.82 (95% CI 0.74–0.84) for operator-1 and operator-2, respecti...
BackgroundGlioblastoma (GBM) is an aggressive malignant brain tumor where median survival is appr... more BackgroundGlioblastoma (GBM) is an aggressive malignant brain tumor where median survival is approximately 15 months after best available multimodal treatment. Recurrence is inevitable, largely due to O6 methylguanine DNA methyltransferase (MGMT) that renders the tumors resistant to temozolomide (TMZ). We hypothesized that pretreatment with bortezomib (BTZ) 48 hours prior to TMZ to deplete MGMT levels would be safe and tolerated by patients with recurrent GBM harboring unmethylated MGMT promoter. The secondary objective was to investigate whether 26S proteasome blockade may enhance differentiation of cytotoxic immune subsets to impact treatment responses measured by radiological criteria and clinical outcomes.MethodsTen patients received intravenous BTZ 1.3 mg/m2 on days 1, 4, and 7 during each 4th weekly TMZ‐chemotherapy starting on day 3 and escalated from 150 mg/m2 per oral 5 days/wk via 175 to 200 mg/m2 in cycles 1, 2, and 3, respectively. Adverse events and quality of life were...
Skull stripping in brain imaging is the removal of the parts of images corresponding to non-brain... more Skull stripping in brain imaging is the removal of the parts of images corresponding to non-brain tissue. Fast and accurate skull stripping is a crucial step for numerous medical brain imaging applications, e.g. registration, segmentation and feature extraction, as it eases subsequent image processing steps. In this work, we propose and compare two novel skull stripping methods based on 2D and 3D convolutional neural networks trained on a large, heterogeneous collection of 2777 clinical 3D T1-weighted MRI images from 1681 healthy subjects. We investigated the performance of the models by testing them on 927 images from 324 subjects set aside from our collection of data, in addition to images from an independent, large brain imaging study: the IXI dataset (n = 556). Our models achieved mean Dice scores higher than 0.978 and Jaccard indices higher than 0.957 on all tests sets, making predictions on new unseen brain MR images in approximately 1.4s for the 3D model and 12.4s for the 2D ...
2016 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), 2016
This paper describes a pilot study aimed at evaluating the usefulness of texture parameters compu... more This paper describes a pilot study aimed at evaluating the usefulness of texture parameters computed from blood pharmacokinetic maps as biomarkers of cancers. The maps are extracted from DCE MRI images which, in turn, visualize blood perfusion in tissue by means of a contrast medium. Each voxel of a DCE image is characterised by a curve in time domain (enhancement signal). The shape of such curves is characteristic to different tissues and is quantified by fitting a proposed parameterized signal model. Different-modality MR images were acquired for a small number of patients, prior to diagnosed cancer surgical extraction. Six images were collected for each patient, four of them were distributions of blood pharmacokinetic model parameters and two — ADC and VIBE MR images. The extracted carcinoma tissue for each patient was investigated by pathologists. This histological grade provided labels to images for supervised data exploration. Both unsupervised (clustering) and supervised (LDA) experiments demonstrated the image texture can be a promising biomarker to reflect correlations between tumour tissue appearance at structural and functional MRI and the corresponding histological characteristics.
In the present study, we investigated differences between dyslexic and normal reading children in... more In the present study, we investigated differences between dyslexic and normal reading children in asymmetry of the planum temporale area in the upper posterior part of the temporal lobe and dichotic listening performance to consonant-vowel syllables. The current study was an extension of previous studies in our laboratory on the same participants, now including also girls and left-handers. There were 20 boys and 3 girls in the dyslexic group and 19 boys and 4 girls in the normal reading group. The age of the participants was 10-12 years for both groups. The participants were screened from a population of 950 students in the fourth school grade in the greater Bergen district. The planum temporale area was measured in sagittal magnetic resonance (MR) images. Mean left and right area and asymmetry index were compared between the groups. Dichotic presentations of consonant-vowel syllables made it possible to separately probe left and right hemisphere phonological function, and to correlate this with planum temporale area. The results showed a significantly larger left than right planum temporale area for both groups. However, while the right planum temporale area was similar for the dyslexic and control groups, the left planum temporale was significantly (one-tailed t-test) smaller in the dyslexic group. Both groups also showed a significant right ear advantage to the consonant-vowel syllables in the dichotic listening test. The relation between planum temporale and dichotic listening asymmetry showed a significant correlation for the dyslexic group only, indicating a positive relation between brain structure and function in dyslexic children. The results are discussed in terms of important subject characteristics with regard to brain markers of dyslexia.
Computer-aided diagnosis of Alzheimer's disease (AD) is a rapidly developing field of neuroim... more Computer-aided diagnosis of Alzheimer's disease (AD) is a rapidly developing field of neuroimaging with strong potential to be used in practice. In this context, assessment of models' robustness to noise and imaging protocol differences together with post-processing and tuning strategies are key tasks to be addressed in order to move towards successful clinical applications. In this study, we investigated the efficacy of Random Forest classifiers trained using different structural MRI measures, with and without neuroanatomical constraints in the detection and prediction of AD in terms of accuracy and between-cohort robustness. From The ADNI database, 185 AD, and 225 healthy controls (HC) were randomly split into training and testing datasets. 165 subjects with mild cognitive impairment (MCI) were distributed according to the month of conversion to dementia (4-year follow-up). Structural 1.5-T MRI-scans were processed using Freesurfer segmentation and cortical reconstruction....
Section for Medical Image Analysis and Pattern Recognition University of Bergen, Arstadveien 19, ... more Section for Medical Image Analysis and Pattern Recognition University of Bergen, Arstadveien 19, N-5009 Bergen, Norway ... In this study we demonstrate an improved differen-tiation of the most common tissue types in the human brain and surrounding structures by quantitative ...
The authors demonstrate an improved differentiation of the most common tissue types in the human ... more The authors demonstrate an improved differentiation of the most common tissue types in the human brain and surrounding structures by quantitative validation using multispectral analysis of magnetic resonance images. This is made possible by a combination of a special training technique and an increase in the number of magnetic resonance channel images with different pulse acquisition parameters. The authors give a description of the tissue-specific multivariate statistical distributions of the pixel intensity values and discuss how their properties may be explored to improve the statistical modeling further. A statistical method to estimate the tissue-specific longitudinal and transverse relaxation times is also given. It is concluded that multispectral analysis of magnetic resonance images is a valuable tool to recognize the most common normal tissue types in the brain and surrounding structures.
Background Tumor burden assessment is essential for radiation therapy (RT), treatment response ev... more Background Tumor burden assessment is essential for radiation therapy (RT), treatment response evaluation, and clinical decision-making. However, manual tumor delineation remains laborious and challenging due to radiological complexity. The objective of this study was to investigate the feasibility of the HD-GLIO tool, an ensemble of pre-trained deep learning models based on the nnUNet-algorithm, for tumor segmentation, response prediction, and its potential for clinical deployment. Methods We analyzed the predicted contrast-enhanced (CE) and non-enhancing (NE) HD-GLIO output in 49 multi-parametric MRI examinations from 23 grade-4 glioma patients. The volumes were retrospectively compared to corresponding manual delineations by 2 independent operators, before prospectively testing the feasibility of clinical deployment of HD-GLIO-output to a RT setting. Results For CE, median Dice scores were 0.81 (95% CI 0.71–0.83) and 0.82 (95% CI 0.74–0.84) for operator-1 and operator-2, respecti...
BackgroundGlioblastoma (GBM) is an aggressive malignant brain tumor where median survival is appr... more BackgroundGlioblastoma (GBM) is an aggressive malignant brain tumor where median survival is approximately 15 months after best available multimodal treatment. Recurrence is inevitable, largely due to O6 methylguanine DNA methyltransferase (MGMT) that renders the tumors resistant to temozolomide (TMZ). We hypothesized that pretreatment with bortezomib (BTZ) 48 hours prior to TMZ to deplete MGMT levels would be safe and tolerated by patients with recurrent GBM harboring unmethylated MGMT promoter. The secondary objective was to investigate whether 26S proteasome blockade may enhance differentiation of cytotoxic immune subsets to impact treatment responses measured by radiological criteria and clinical outcomes.MethodsTen patients received intravenous BTZ 1.3 mg/m2 on days 1, 4, and 7 during each 4th weekly TMZ‐chemotherapy starting on day 3 and escalated from 150 mg/m2 per oral 5 days/wk via 175 to 200 mg/m2 in cycles 1, 2, and 3, respectively. Adverse events and quality of life were...
Skull stripping in brain imaging is the removal of the parts of images corresponding to non-brain... more Skull stripping in brain imaging is the removal of the parts of images corresponding to non-brain tissue. Fast and accurate skull stripping is a crucial step for numerous medical brain imaging applications, e.g. registration, segmentation and feature extraction, as it eases subsequent image processing steps. In this work, we propose and compare two novel skull stripping methods based on 2D and 3D convolutional neural networks trained on a large, heterogeneous collection of 2777 clinical 3D T1-weighted MRI images from 1681 healthy subjects. We investigated the performance of the models by testing them on 927 images from 324 subjects set aside from our collection of data, in addition to images from an independent, large brain imaging study: the IXI dataset (n = 556). Our models achieved mean Dice scores higher than 0.978 and Jaccard indices higher than 0.957 on all tests sets, making predictions on new unseen brain MR images in approximately 1.4s for the 3D model and 12.4s for the 2D ...
2016 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), 2016
This paper describes a pilot study aimed at evaluating the usefulness of texture parameters compu... more This paper describes a pilot study aimed at evaluating the usefulness of texture parameters computed from blood pharmacokinetic maps as biomarkers of cancers. The maps are extracted from DCE MRI images which, in turn, visualize blood perfusion in tissue by means of a contrast medium. Each voxel of a DCE image is characterised by a curve in time domain (enhancement signal). The shape of such curves is characteristic to different tissues and is quantified by fitting a proposed parameterized signal model. Different-modality MR images were acquired for a small number of patients, prior to diagnosed cancer surgical extraction. Six images were collected for each patient, four of them were distributions of blood pharmacokinetic model parameters and two — ADC and VIBE MR images. The extracted carcinoma tissue for each patient was investigated by pathologists. This histological grade provided labels to images for supervised data exploration. Both unsupervised (clustering) and supervised (LDA) experiments demonstrated the image texture can be a promising biomarker to reflect correlations between tumour tissue appearance at structural and functional MRI and the corresponding histological characteristics.
In the present study, we investigated differences between dyslexic and normal reading children in... more In the present study, we investigated differences between dyslexic and normal reading children in asymmetry of the planum temporale area in the upper posterior part of the temporal lobe and dichotic listening performance to consonant-vowel syllables. The current study was an extension of previous studies in our laboratory on the same participants, now including also girls and left-handers. There were 20 boys and 3 girls in the dyslexic group and 19 boys and 4 girls in the normal reading group. The age of the participants was 10-12 years for both groups. The participants were screened from a population of 950 students in the fourth school grade in the greater Bergen district. The planum temporale area was measured in sagittal magnetic resonance (MR) images. Mean left and right area and asymmetry index were compared between the groups. Dichotic presentations of consonant-vowel syllables made it possible to separately probe left and right hemisphere phonological function, and to correlate this with planum temporale area. The results showed a significantly larger left than right planum temporale area for both groups. However, while the right planum temporale area was similar for the dyslexic and control groups, the left planum temporale was significantly (one-tailed t-test) smaller in the dyslexic group. Both groups also showed a significant right ear advantage to the consonant-vowel syllables in the dichotic listening test. The relation between planum temporale and dichotic listening asymmetry showed a significant correlation for the dyslexic group only, indicating a positive relation between brain structure and function in dyslexic children. The results are discussed in terms of important subject characteristics with regard to brain markers of dyslexia.
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