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Smedby, Örjan, ProfessorORCID iD iconorcid.org/0000-0002-7750-1917
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Publications (10 of 61) Show all publications
Kataria, B., Nilsson Althén, J., Smedby, Ö., Persson, A., Sökjer, H. & Sandborg, M. (2020). Assessment of image quality in abdominal computed tomography: Effect of model-based iterative reconstruction, multi-planar reconstruction and slice thickness on potential dose reduction. European Journal of Radiology, 122, Article ID 108703.
Open this publication in new window or tab >>Assessment of image quality in abdominal computed tomography: Effect of model-based iterative reconstruction, multi-planar reconstruction and slice thickness on potential dose reduction
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2020 (English)In: European Journal of Radiology, ISSN 0720-048X, E-ISSN 1872-7727, Vol. 122, article id 108703Article in journal (Refereed) Published
Abstract [en]

Purpose: To determine the effect of tube load, model-based iterative reconstruction (MBIR) strength and slice thickness in abdominal CT using visual comparison of multi-planar reconstruction images. Method: Five image criteria were assessed independently by four radiologists on two data sets at 42- and 98-mAs tube loads for 25 patients examined on a 192-slice dual-source CT scanner. Effect of tube load, MBIR strength, slice thickness and potential dose reduction was estimated with Visual Grading Regression (VGR). Objective image quality was determined by measuring noise (SD), contrast-to-noise (CNR) ratio and noise-power spectra (NPS). Results: Comparing 42- and 98-mAs tube loads, improved image quality was observed as a strong effect of log tube load regardless of MBIR strength (p < 0.001). Comparing strength 5 to 3, better image quality was obtained for two criteria (p < 0.01), but inferior for liver parenchyma and overall image quality. Image quality was significantly better for slice thicknesses of 2mm and 3mm compared to 1mm, with potential dose reductions between 24%-41%. As expected, with decrease in slice thickness and algorithm strength, the noise power and SD (HU-values) increased, while the CNR decreased. Conclusion: Increasing slice thickness from 1 mm to 2 mm or 3 mm allows for a possible dose reduction. MBIR strength 5 shows improved image quality for three out of five criteria for 1 mm slice thickness. Increasing MBIR strength from 3 to 5 has diverse effects on image quality. Our findings do not support a general recommendation to replace strength 3 by strength 5 in clinical abdominal CT protocols. However, strength 5 may be used in task-based protocols.

Place, publisher, year, edition, pages
Elsevier Ireland Ltd, 2020
Keywords
Computed tomography, Abdomen, Iterative reconstruction, Dose, Slice thickness, Multi-planar reconstruction (MPR)
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:kth:diva-266743 (URN)10.1016/j.ejrad.2019.108703 (DOI)000505150900002 ()31810641 (PubMedID)2-s2.0-85076199636 (Scopus ID)
Note

QC 20200117

Available from: 2020-01-17 Created: 2020-01-17 Last updated: 2020-01-17Bibliographically approved
Mahbod, A., Schaefer, G., Ellinger, I., Ecker, R., Smedby, Ö. & Wang, C. (2019). A Two-Stage U-Net Algorithm for Segmentation of Nuclei in H&E-Stained Tissues. In: Constantino Carlos Reyes-Aldasoro, Andrew Janowczyk, Mitko Veta, Peter Bankhead, Korsuk Sirinukunwattana (Ed.), Digital Pathology: 15th European Congress, ECDP 2019, Warwick, UK, April 10–13, 2019, Proceedings. Paper presented at 15th European Congress on Digital Pathology, ECDP 2019, Warwick, United Kingdom 10-13 April 2019 (pp. 75-82). Springer Verlag
Open this publication in new window or tab >>A Two-Stage U-Net Algorithm for Segmentation of Nuclei in H&E-Stained Tissues
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2019 (English)In: Digital Pathology: 15th European Congress, ECDP 2019, Warwick, UK, April 10–13, 2019, Proceedings / [ed] Constantino Carlos Reyes-Aldasoro, Andrew Janowczyk, Mitko Veta, Peter Bankhead, Korsuk Sirinukunwattana, Springer Verlag , 2019, p. 75-82Conference paper, Published paper (Refereed)
Abstract [en]

Nuclei segmentation is an important but challenging task in the analysis of hematoxylin and eosin (H&E)-stained tissue sections. While various segmentation methods have been proposed, machine learning-based algorithms and in particular deep learning-based models have been shown to deliver better segmentation performance. In this work, we propose a novel approach to segment touching nuclei in H&E-stained microscopic images using U-Net-based models in two sequential stages. In the first stage, we perform semantic segmentation using a classification U-Net that separates nuclei from the background. In the second stage, the distance map of each nucleus is created using a regression U-Net. The final instance segmentation masks are then created using a watershed algorithm based on the distance maps. Evaluated on a publicly available dataset containing images from various human organs, the proposed algorithm achieves an average aggregate Jaccard index of 56.87%, outperforming several state-of-the-art algorithms applied on the same dataset.

Place, publisher, year, edition, pages
Springer Verlag, 2019
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349
Keywords
Deep learning, Digital pathology, Nuclei segmentation, Tissue analysis, U-Net, Machine learning, Pathology, Semantics, Tissue, Digital pathologies, Learning Based Models, Segmentation methods, Segmentation performance, Semantic segmentation, State-of-the-art algorithms, Image segmentation
National Category
Computer Sciences
Identifiers
urn:nbn:se:kth:diva-262448 (URN)10.1007/978-3-030-23937-4_9 (DOI)2-s2.0-85069146581 (Scopus ID)9783030239367 (ISBN)
Conference
15th European Congress on Digital Pathology, ECDP 2019, Warwick, United Kingdom 10-13 April 2019
Note

QC 20191021

Available from: 2019-10-21 Created: 2019-10-21 Last updated: 2019-10-21Bibliographically approved
Bendazzoli, S., Brusini, I., Damberg, P., Smedby, Ö., Andersson, L. & Wang, C. (2019). Automatic rat brain segmentation from MRI using statistical shape models and random forest. In: Angelini, ED Landman, BA (Ed.), MEDICAL IMAGING 2019: IMAGE PROCESSING. Paper presented at Conference on Medical Imaging: Image Processing, FEB 19-21, 2019, San Diego, CA. SPIE-INT SOC OPTICAL ENGINEERING, Article ID 1094920.
Open this publication in new window or tab >>Automatic rat brain segmentation from MRI using statistical shape models and random forest
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2019 (English)In: MEDICAL IMAGING 2019: IMAGE PROCESSING / [ed] Angelini, ED Landman, BA, SPIE-INT SOC OPTICAL ENGINEERING , 2019, article id 1094920Conference paper, Published paper (Refereed)
Abstract [en]

In MRI neuroimaging, the shimming procedure is used before image acquisition to correct for inhomogeneity of the static magnetic field within the brain. To correctly adjust the field, the brain's location and edges must first be identified from quickly-acquired low resolution data. This process is currently carried out manually by an operator, which can be time-consuming and not always accurate. In this work, we implement a quick and automatic technique for brain segmentation to be potentially used during the shimming. Our method is based on two main steps. First, a random forest classifier is used to get a preliminary segmentation from an input MRI image. Subsequently, a statistical shape model of the brain, which was previously generated from ground-truth segmentations, is fitted to the output of the classifier to obtain a model-based segmentation mask. In this way, a-priori knowledge on the brain's shape is included in the segmentation pipeline. The proposed methodology was tested on low resolution images of rat brains and further validated on rabbit brain images of higher resolution. Our results suggest that the present method is promising for the desired purpose in terms of time efficiency, segmentation accuracy and repeatability. Moreover, the use of shape modeling was shown to be particularly useful when handling low-resolution data, which could lead to erroneous classifications when using only machine learning-based methods.

Place, publisher, year, edition, pages
SPIE-INT SOC OPTICAL ENGINEERING, 2019
Series
Proceedings of SPIE, ISSN 0277-786X ; 10949
Keywords
brain MRI, image segmentation, shimming, random forest, statistical shape model
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:kth:diva-260221 (URN)10.1117/12.2512409 (DOI)000483012700090 ()2-s2.0-85068344757 (Scopus ID)978-1-5106-2546-4 (ISBN)
Conference
Conference on Medical Imaging: Image Processing, FEB 19-21, 2019, San Diego, CA
Note

QC 20190930

Available from: 2019-09-30 Created: 2019-09-30 Last updated: 2019-09-30Bibliographically approved
Astaraki, M., Wang, C., Buizza, G., Toma-Dasu, I., Lazzeroni, M. & Smedby, Ö. (2019). Early survival prediction in non-small cell lung cancer from PET/CT images using an intra-tumor partitioning method. Physica medica (Testo stampato), 60, 58-65
Open this publication in new window or tab >>Early survival prediction in non-small cell lung cancer from PET/CT images using an intra-tumor partitioning method
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2019 (English)In: Physica medica (Testo stampato), ISSN 1120-1797, E-ISSN 1724-191X, Vol. 60, p. 58-65Article in journal (Refereed) Published
Abstract [en]

Purpose: To explore prognostic and predictive values of a novel quantitative feature set describing intra-tumor heterogeneity in patients with lung cancer treated with concurrent and sequential chemoradiotherapy. Methods: Longitudinal PET-CT images of 30 patients with non-small cell lung cancer were analysed. To describe tumor cell heterogeneity, the tumors were partitioned into one to ten concentric regions depending on their sizes, and, for each region, the change in average intensity between the two scans was calculated for PET and CT images separately to form the proposed feature set. To validate the prognostic value of the proposed method, radiomics analysis was performed and a combination of the proposed novel feature set and the classic radiomic features was evaluated. A feature selection algorithm was utilized to identify the optimal features, and a linear support vector machine was trained for the task of overall survival prediction in terms of area under the receiver operating characteristic curve (AUROC). Results: The proposed novel feature set was found to be prognostic and even outperformed the radiomics approach with a significant difference (AUROC(sALop) = 0.90 vs. AUROC(radiomic) = 0.71) when feature selection was not employed, whereas with feature selection, a combination of the novel feature set and radiomics led to the highest prognostic values. Conclusion: A novel feature set designed for capturing intra-tumor heterogeneity was introduced. Judging by their prognostic power, the proposed features have a promising potential for early survival prediction.

Place, publisher, year, edition, pages
ELSEVIER SCI LTD, 2019
Keywords
Survival prediction, Treatment response, Radiomics, Tumor heterogeneity, LONG ER, 1988, BIOMETRICS, V44, P837
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:kth:diva-251338 (URN)10.1016/j.ejmp.2019.03.024 (DOI)000464560200009 ()31000087 (PubMedID)2-s2.0-85063364742 (Scopus ID)
Note

QC 20190523

Available from: 2019-05-23 Created: 2019-05-23 Last updated: 2019-10-09Bibliographically approved
Astaraki, M., Wang, C., Buizza, G., Toma-Dasu, I., Lazzeroni, M. & Smedby, Ö. (2019). Early survival prediction in non-small cell lung cancer with PET/CT size aware longitudinal pattern. Paper presented at 38th Annual Meeting of the European-Society-for-Radiotherapy-and-Oncology (ESTRO), APR 26-30, 2019, Milan, ITALY. Radiotherapy and Oncology, 133, S208-S209
Open this publication in new window or tab >>Early survival prediction in non-small cell lung cancer with PET/CT size aware longitudinal pattern
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2019 (English)In: Radiotherapy and Oncology, ISSN 0167-8140, E-ISSN 1879-0887, ISSN 0167-8140, Vol. 133, p. S208-S209Article in journal (Refereed) Published
Keywords
Oncology; Radiology, Nuclear Medicine & Medical Imaging
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:kth:diva-252991 (URN)10.1016/S0167-8140(19)30826-6 (DOI)000468315601037 ()
Conference
38th Annual Meeting of the European-Society-for-Radiotherapy-and-Oncology (ESTRO), APR 26-30, 2019, Milan, ITALY
Note

QC 20190729

Available from: 2019-07-29 Created: 2019-07-29 Last updated: 2019-09-30Bibliographically approved
Zhuang, X., Li, L., Payer, C., Štern, D., Urschler, M., Heinrich, M. P., . . . Yang, G. (2019). Evaluation of algorithms for Multi-Modality Whole Heart Segmentation: An open-access grand challenge.. Medical Image Analysis, 58, Article ID 101537.
Open this publication in new window or tab >>Evaluation of algorithms for Multi-Modality Whole Heart Segmentation: An open-access grand challenge.
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2019 (English)In: Medical Image Analysis, ISSN 1361-8415, E-ISSN 1361-8423, Vol. 58, article id 101537Article in journal (Refereed) Published
Abstract [en]

Knowledge of whole heart anatomy is a prerequisite for many clinical applications. Whole heart segmentation (WHS), which delineates substructures of the heart, can be very valuable for modeling and analysis of the anatomy and functions of the heart. However, automating this segmentation can be challenging due to the large variation of the heart shape, and different image qualities of the clinical data. To achieve this goal, an initial set of training data is generally needed for constructing priors or for training. Furthermore, it is difficult to perform comparisons between different methods, largely due to differences in the datasets and evaluation metrics used. This manuscript presents the methodologies and evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017. The challenge provided 120 three-dimensional cardiac images covering the whole heart, including 60 CT and 60 MRI volumes, all acquired in clinical environments with manual delineation. Ten algorithms for CT data and eleven algorithms for MRI data, submitted from twelve groups, have been evaluated. The results showed that the performance of CT WHS was generally better than that of MRI WHS. The segmentation of the substructures for different categories of patients could present different levels of challenge due to the difference in imaging and variations of heart shapes. The deep learning (DL)-based methods demonstrated great potential, though several of them reported poor results in the blinded evaluation. Their performance could vary greatly across different network structures and training strategies. The conventional algorithms, mainly based on multi-atlas segmentation, demonstrated good performance, though the accuracy and computational efficiency could be limited. The challenge, including provision of the annotated training data and the blinded evaluation for submitted algorithms on the test data, continues as an ongoing benchmarking resource via its homepage (www.sdspeople.fudan.edu.cn/zhuangxiahai/0/mmwhs/).

Keywords
Benchmark, Challenge, Multi-modality, Whole Heart Segmentation
National Category
Medical Image Processing
Identifiers
urn:nbn:se:kth:diva-258723 (URN)10.1016/j.media.2019.101537 (DOI)000496605700010 ()31446280 (PubMedID)2-s2.0-85070924419 (Scopus ID)
Note

QC 20190911. QC 20200109

Available from: 2019-09-10 Created: 2019-09-10 Last updated: 2020-01-09Bibliographically approved
Kataria, B., Althén, J. N., Smedby, Ö., Persson, A., Sökjer, H. & Sandborg, M. (2019). Image quality and pathology assessment in CT Urography: when is the low-dose series sufficient?. BMC Medical Imaging, 19(1), Article ID 64.
Open this publication in new window or tab >>Image quality and pathology assessment in CT Urography: when is the low-dose series sufficient?
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2019 (English)In: BMC Medical Imaging, ISSN 1471-2342, E-ISSN 1471-2342, Vol. 19, no 1, article id 64Article in journal (Refereed) Published
Abstract [en]

Background Our aim was to compare CT images from native, nephrographic and excretory phases using image quality criteria as well as the detection of positive pathological findings in CT Urography, to explore if the radiation burden to the younger group of patients or patients with negative outcomes can be reduced. Methods This is a retrospective study of 40 patients who underwent a CT Urography examination on a 192-slice dual source scanner. Image quality was assessed for four specific renal image criteria from the European guidelines, together with pathological assessment in three categories: renal, other abdominal, and incidental findings without clinical significance. Each phase was assessed individually by three radiologists with varying experience using a graded scale. Certainty scores were derived based on the graded assessments. Statistical analysis was performed using visual grading regression (VGR). The limit for significance was set at p = 0.05. Results For visual reproduction of the renal parenchyma and renal arteries, the image quality was judged better for the nephrogram phase (p < 0.001), whereas renal pelvis/calyces and proximal ureters were better reproduced in the excretory phase compared to the native phase (p < 0.001). Similarly, significantly higher certainty scores were obtained in the nephrogram phase for renal parenchyma and renal arteries, but in the excretory phase for renal pelvis/calyxes and proximal ureters. Assessment of pathology in the three categories showed no statistically significant differences between the three phases. Certainty scores for assessment of pathology, however, showed a significantly higher certainty for renal pathology when comparing the native phase to nephrogram and excretory phase and a significantly higher score for nephrographic phase but only for incidental findings. Conclusion Visualisation of renal anatomy was as expected with each post-contrast phase showing favourable scores compared to the native phase. No statistically significant differences in the assessment of pathology were found between the three phases. The low-dose CT (LDCT) seems to be sufficient in differentiating between normal and pathological examinations. To reduce the radiation burden in certain patient groups, the LDCT could be considered a suitable alternative as a first line imaging method. However, radiologists should be aware of its limitations.

Place, publisher, year, edition, pages
BMC, 2019
Keywords
Computed tomography, Urography, Low-dose, Optimization, Image quality, Dose
National Category
Medical Image Processing
Identifiers
urn:nbn:se:kth:diva-257446 (URN)10.1186/s12880-019-0363-z (DOI)000480486200001 ()31399078 (PubMedID)2-s2.0-85070460822 (Scopus ID)
Note

QC 20190902

Available from: 2019-09-02 Created: 2019-09-02 Last updated: 2019-09-02Bibliographically approved
Astaraki, M., Toma-Dasu, I., Smedby, Ö. & Wang, C. (2019). Normal Appearance Autoencoder for Lung Cancer Detection and Segmentation. In: 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019: . Paper presented at 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019; Shenzhen; China; 13 October 2019 through 17 October 2019 (pp. 249-256). Springer, 11769
Open this publication in new window or tab >>Normal Appearance Autoencoder for Lung Cancer Detection and Segmentation
2019 (English)In: 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, Springer, 2019, Vol. 11769, p. 249-256Conference paper, Published paper (Refereed)
Abstract [en]

One of the major differences between medical doctor training and machine learning is that doctors are trained to recognize normal/healthy anatomy first. Knowing the healthy appearance of anatomy structures helps doctors to make better judgement when some abnormality shows up in an image. In this study, we propose a normal appearance autoencoder (NAA), that removes abnormalities from a diseased image. This autoencoder is semi-automatically trained using another partial convolutional in-paint network that is trained using healthy subjects only. The output of the autoencoder is then fed to a segmentation net in addition to the original input image, i.e. the latter gets both the diseased image and a simulated healthy image where the lesion is artificially removed. By getting access to knowledge of how the abnormal region is supposed to look, we hypothesized that the segmentation network could perform better than just being shown the original slice. We tested the proposed network on the LIDC-IDRI dataset for lung cancer detection and segmentation. The preliminary results show the NAA approach improved segmentation accuracy substantially in comparison with the conventional U-Net architecture.

Place, publisher, year, edition, pages
Springer, 2019
Series
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN 0302-9743 ; 11769
Keywords
Anomaly detection, Convolutional variational autoencoder, Lung nodule segmentation
National Category
Medical Engineering
Identifiers
urn:nbn:se:kth:diva-266080 (URN)10.1007/978-3-030-32226-7_28 (DOI)2-s2.0-85075827836 (Scopus ID)9783030322250 (ISBN)
Conference
22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019; Shenzhen; China; 13 October 2019 through 17 October 2019
Note

QC 20191220

Available from: 2019-12-20 Created: 2019-12-20 Last updated: 2019-12-20Bibliographically approved
Wan, F., Smedby, Ö. & Wang, C. (2019). Simultaneous MR knee image segmentation and bias field correction using deep learning and partial convolution. In: Medical Imaging 2019: Image Processing. Paper presented at Medical Imaging 2019: Image Processing; San Diego; United States; 19 February 2019 through 21 February 2019. SPIE - International Society for Optical Engineering, 10949, Article ID 1094909.
Open this publication in new window or tab >>Simultaneous MR knee image segmentation and bias field correction using deep learning and partial convolution
2019 (English)In: Medical Imaging 2019: Image Processing, SPIE - International Society for Optical Engineering, 2019, Vol. 10949, article id 1094909Conference paper, Published paper (Refereed)
Abstract [en]

Intensity inhomogeneity is a great challenge for automated organ segmentation in magnetic resonance (MR) images. Many segmentation methods fail to deliver satisfactory results when the images are corrupted by a bias field. Although inhomogeneity correction methods exist, they often fail to remove the bias field completely in knee MR images. We present a new iterative approach that simultaneously predicts the segmentation mask of knee structures using a 3D U-net and estimates the bias field in 3D MR knee images using partial convolution operations. First, the test images run through a trained 3D U-net to generate a preliminary segmentation result, which is then fed to the partial convolution filter to create a preliminary estimation of the bias field using the segmented bone mask. Finally, the estimated bias field is then used to produce bias field corrected images as the new inputs to the 3D U-net. Through this loop, the segmentation results and bias field correction are iteratively improved. The proposed method was evaluated on 20 proton-density (PD)-weighted knee MRI scans with manually created segmentation ground truth using 10 fold cross-validation. In our preliminary experiments, the proposed methods outperformed conventional inhomogeneity-correction-plus-segmentation setup in terms of both segmentation accuracy and speed.

Place, publisher, year, edition, pages
SPIE - International Society for Optical Engineering, 2019
Series
Progress in Biomedical Optics and Imaging - Proceedings of SPIE, ISSN 1605-7422 ; 10949
National Category
Medical Image Processing
Identifiers
urn:nbn:se:kth:diva-258892 (URN)10.1117/12.2512950 (DOI)000483012700008 ()2-s2.0-85068319000 (Scopus ID)9781510625457 (ISBN)
Conference
Medical Imaging 2019: Image Processing; San Diego; United States; 19 February 2019 through 21 February 2019
Note

QC 20190913

Available from: 2019-09-11 Created: 2019-09-11 Last updated: 2019-09-26Bibliographically approved
Brusini, I., Jörgens, D., Smedby, Ö. & Moreno, R. (2019). Voxel-Wise Clustering of Tractography Data for Building Atlases of Local Fiber Geometry. In: : . Paper presented at International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018; Granada; Spain; 20 September 2018 through 20 September 2018 (pp. 345-357).
Open this publication in new window or tab >>Voxel-Wise Clustering of Tractography Data for Building Atlases of Local Fiber Geometry
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

This paper aims at proposing a method to generate atlases of white matter fibers’ geometry that consider local orientation and curvature of fibers extracted from tractography data. Tractography was performed on diffusion magnetic resonance images from a set of healthy subjects and each tract was characterized voxel-wise by its curvature and Frenet–Serret frame, based on which similar tracts could be clustered separately for each voxel and each subject. Finally, the centroids of the clusters identified in all subjects were clustered to create the final atlas. The proposed clustering technique showed promising results in identifying voxel-wise distributions of curvature and orientation. Two tractography algorithms (one deterministic and one probabilistic) were tested for the present work, obtaining two different atlases. A high agreement between the two atlases was found in several brain regions. This suggests that more advanced tractography methods might only be required for some specific regions in the brain. In addition, the probabilistic approach resulted in the identification of a higher number of fiber orientations in various white matter areas, suggesting it to be more adequate for investigating complex fiber configurations in the proposed framework as compared to deterministic tractography.

National Category
Medical Image Processing
Identifiers
urn:nbn:se:kth:diva-259768 (URN)10.1007/978-3-030-05831-9_27 (DOI)000493062700027 ()2-s2.0-85066883835 (Scopus ID)
Conference
International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018; Granada; Spain; 20 September 2018 through 20 September 2018
Note

QC 20190923

Available from: 2019-09-23 Created: 2019-09-23 Last updated: 2020-01-02Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0002-7750-1917

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