Örjan Smedby

Verified email at kth.se

Professor of medical image processing and visualization, Department of Biomedical Engineering and Health Systems
KTH Royal Institute of Technology



                             

https://researchid.co/orjansmedby

RESEARCH INTERESTS

Medical image processing
Machine learning
Medical visualization

172

Scopus Publications

6439

Scholar Citations

41

Scholar h-index

112

Scholar i10-index

Scopus Publications

  • MRI-derived brain age as a biomarker of ageing in rats: validation using a healthy lifestyle intervention
    Irene Brusini, Eilidh MacNicol, Eugene Kim, Örjan Smedby, Chunliang Wang, Eric Westman, Mattia Veronese, Federico Turkheimer, and Diana Cash

    Neurobiology of Aging, ISSN: 01974580, eISSN: 15581497, Volume: 109, Pages: 204-215, Published: January 2022 Elsevier BV
    The difference between brain age predicted from MRI and chronological age (the so-called BrainAGE) has been proposed as an ageing biomarker. We analyse its cross-species potential by testing it on rats undergoing an ageing modulation intervention. Our rat brain age prediction model combined Gaussian process regression with a classifier and achieved a mean absolute error (MAE) of 4.87 weeks using cross-validation on a longitudinal dataset of 31 normal ageing rats. It was then tested on two groups of 24 rats (MAE = 9.89 weeks, correlation coefficient = 0.86): controls vs. a group under long-term environmental enrichment and dietary restriction (EEDR). Using a linear mixed-effects model, BrainAGE was found to increase more slowly with chronological age in EEDR rats (p=0.015 for the interaction term). Cox regression showed that older BrainAGE at 5 months was associated with higher mortality risk (p=0.03). Our findings suggest that lifestyle-related prevention approaches may help to slow down brain ageing in rodents and the potential of BrainAGE as a predictor of age-related health outcomes.

  • A Comparative Study of Radiomics and Deep-Learning Based Methods for Pulmonary Nodule Malignancy Prediction in Low Dose CT Images
    Mehdi Astaraki, Guang Yang, Yousuf Zakko, Iuliana Toma-Dasu, Örjan Smedby, and Chunliang Wang

    Frontiers in Oncology, eISSN: 2234943X, Published: 17 December 2021 Frontiers Media SA
    ObjectivesBoth radiomics and deep learning methods have shown great promise in predicting lesion malignancy in various image-based oncology studies. However, it is still unclear which method to choose for a specific clinical problem given the access to the same amount of training data. In this study, we try to compare the performance of a series of carefully selected conventional radiomics methods, end-to-end deep learning models, and deep-feature based radiomics pipelines for pulmonary nodule malignancy prediction on an open database that consists of 1297 manually delineated lung nodules.MethodsConventional radiomics analysis was conducted by extracting standard handcrafted features from target nodule images. Several end-to-end deep classifier networks, including VGG, ResNet, DenseNet, and EfficientNet were employed to identify lung nodule malignancy as well. In addition to the baseline implementations, we also investigated the importance of feature selection and class balancing, as well as separating the features learned in the nodule target region and the background/context region. By pooling the radiomics and deep features together in a hybrid feature set, we investigated the compatibility of these two sets with respect to malignancy prediction.ResultsThe best baseline conventional radiomics model, deep learning model, and deep-feature based radiomics model achieved AUROC values (mean ± standard deviations) of 0.792 ± 0.025, 0.801 ± 0.018, and 0.817 ± 0.032, respectively through 5-fold cross-validation analyses. However, after trying out several optimization techniques, such as feature selection and data balancing, as well as adding context features, the corresponding best radiomics, end-to-end deep learning, and deep-feature based models achieved AUROC values of 0.921 ± 0.010, 0.824 ± 0.021, and 0.936 ± 0.011, respectively. We achieved the best prediction accuracy from the hybrid feature set (AUROC: 0.938 ± 0.010).ConclusionThe end-to-end deep-learning model outperforms conventional radiomics out of the box without much fine-tuning. On the other hand, fine-tuning the models lead to significant improvements in the prediction performance where the conventional and deep-feature based radiomics models achieved comparable results. The hybrid radiomics method seems to be the most promising model for lung nodule malignancy prediction in this comparative study.

  • IMAGE QUALITY AND POTENTIAL DOSE REDUCTION USING ADVANCED MODELED ITERATIVE RECONSTRUCTION (ADMIRE) IN ABDOMINAL CT - A REVIEW
    B Kataria, J Nilsson Althén, Ö Smedby, A Persson, H Sökjer, and M Sandborg

    Radiation Protection Dosimetry, ISSN: 01448420, eISSN: 17423406, Volume: 195, Issue: 3-4, Pages: 177-187, Published: 1 October 2021 Oxford University Press (OUP)
    Abstract Traditional filtered back projection (FBP) reconstruction methods have served the computed tomography (CT) community well for over 40 years. With the increased use of CT during the last decades, efforts to minimise patient exposure, while maintaining sufficient or improved image quality, have led to the development of model-based iterative reconstruction (MBIR) algorithms from several vendors. The usefulness of the advanced modeled iterative reconstruction (ADMIRE) (Siemens Healthineers) MBIR in abdominal CT is reviewed and its noise suppression and/or dose reduction possibilities explored. Quantitative and qualitative methods with phantom and human subjects were used. Assessment of the quality of phantom images will not always correlate positively with those of patient images, particularly at the higher strength of the ADMIRE algorithm. With few exceptions, ADMIRE Strength 3 typically allows for substantial noise reduction compared to FBP and hence to significant (≈30%) patient dose reductions. The size of the dose reductions depends on the diagnostic task.

  • Benign-malignant pulmonary nodule classification in low-dose CT with convolutional features
    Mehdi Astaraki, Yousuf Zakko, Iuliana Toma Dasu, Örjan Smedby, and Chunliang Wang

    Physica Medica, ISSN: 11201797, eISSN: 1724191X, Pages: 146-153, Published: March 2021 Elsevier BV
    PURPOSE Low-Dose Computed Tomography (LDCT) is the most common imaging modality for lung cancer diagnosis. The presence of nodules in the scans does not necessarily portend lung cancer, as there is an intricate relationship between nodule characteristics and lung cancer. Therefore, benign-malignant pulmonary nodule classification at early detection is a crucial step to improve diagnosis and prolong patient survival. The aim of this study is to propose a method for predicting nodule malignancy based on deep abstract features. METHODS To efficiently capture both intra-nodule heterogeneities and contextual information of the pulmonary nodules, a dual pathway model was developed to integrate the intra-nodule characteristics with contextual attributes. The proposed approach was implemented with both supervised and unsupervised learning schemes. A random forest model was added as a second component on top of the networks to generate the classification results. The discrimination power of the model was evaluated by calculating the Area Under the Receiver Operating Characteristic Curve (AUROC) metric. RESULTS Experiments on 1297 manually segmented nodules show that the integration of context and target supervised deep features have a great potential for accurate prediction, resulting in a discrimination power of 0.936 in terms of AUROC, which outperformed the classification performance of the Kaggle 2017 challenge winner. CONCLUSION Empirical results demonstrate that integrating nodule target and context images into a unified network improves the discrimination power, outperforming the conventional single pathway convolutional neural networks.

  • Deriving brain imaging biomarkers with deep learning
    Örjan Smedby

    Proceedings of SPIE - The International Society for Optical Engineering, ISSN: 0277786X, eISSN: 1996756X, Volume: 11804, Published: 2021 SPIE
    A central research topic in medical image processing is the development of imaging biomarkers, i.e. image-based numeric measures of the degree (or probability) of disease. Typically, they rely on segmentation of an anatomical or pathological structure in a radiological image, followed by quantitative measurement. With much of traditional image processing methods being supplanted by machine learning techniques, the identification of new imaging biomarkers is also often made with such techniques, in particular deep learning. Successful examples include quantitative assessment of Alzheimer’s disease and Parkinson’s disease based on brain MRI data, as well as image-based brain age estimation.

  • Quantitative MRI using relaxometry in malignant gliomas detects contrast enhancement in peritumoral oedema
    I. Blystad, J. B. M. Warntjes, Ö Smedby, P. Lundberg, E.-M. Larsson, and A. Tisell

    Scientific Reports, eISSN: 20452322, Published: 1 December 2020 Springer Science and Business Media LLC
    Abstract Malignant gliomas are primary brain tumours with an infiltrative growth pattern, often with contrast enhancement on magnetic resonance imaging (MRI). However, it is well known that tumour infiltration extends beyond the visible contrast enhancement. The aim of this study was to investigate if there is contrast enhancement not detected visually in the peritumoral oedema of malignant gliomas by using relaxometry with synthetic MRI. 25 patients who had brain tumours with a radiological appearance of malignant glioma were prospectively included. A quantitative MR-sequence measuring longitudinal relaxation (R1), transverse relaxation (R2) and proton density (PD), was added to the standard MRI protocol before surgery. Five patients were excluded, and in 20 patients, synthetic MR images were created from the quantitative scans. Manual regions of interest (ROIs) outlined the visibly contrast-enhancing border of the tumours and the peritumoral area. Contrast enhancement was quantified by subtraction of native images from post GD-images, creating an R1-difference-map. The quantitative R1-difference-maps showed significant contrast enhancement in the peritumoral area (0.047) compared to normal appearing white matter (0.032), p = 0.048. Relaxometry detects contrast enhancement in the peritumoral area of malignant gliomas. This could represent infiltrative tumour growth.

  • A comparative study of trabecular bone micro-structural measurements using different CT modalities
    Indranil Guha, Benjamin Klintström, Eva Klintström, Xiaoliu Zhang, Örjan Smedby, Rodrigo Moreno, and Punam K Saha

    Physics in Medicine and Biology, ISSN: 00319155, eISSN: 13616560, Published: 25 November 2020 IOP Publishing
    Osteoporosis, characterized by reduced bone mineral density and micro-architectural degeneration, significantly enhances fracture-risk. There are several viable methods for trabecular bone micro-imaging, which widely vary in terms of technology, reconstruction principle, spatial resolution, and acquisition time. We have performed an excised cadaveric bone specimen study to evaluate different CT-imaging modalities for trabecular bone micro-structural analysis. Excised cadaveric bone specimens from the distal radius were scanned using micro-CT and four in vivo CT imaging modalities: HR-pQCT, dental CBCT, whole-body MDCT, and extremity CBCT. A new algorithm was developed to optimize soft thresholding parameters for individual in vivo CT modalities for computing quantitative bone volume fraction maps. Finally, agreement of trabecular bone micro-structural measures, derived from different in vivo CT imaging, with reference measures from micro-CT imaging was examined. Observed values of most trabecular measures, including trabecular bone volume, network area, transverse and plate-rod micro-structure, thickness, and spacing, for in vivo CT modalities were higher than their micro-CT-based reference values. In general, HR-pQCT-based trabecular bone measures were closer to their reference values as compared to other in vivo CT modalities. Despite large differences in observed values of measures among modalities, high linear correlation (r ∈ [0.94 0.99]) was found between micro-CT and in vivo CT-derived measures of trabecular bone volume, transverse and plate micro-structural volume, and network area. All HR-pQCT-derived trabecular measures, except the erosion index, showed high correlation (r ∈ [0.91 0.99]). The plate-width measure showed a higher correlation (r ∈ [0.72 0.91]) among in vivo and micro-CT modalities than its counterpart binary plate-rod characterization-based measure erosion index (r ∈ [0.65 0.81]). Although a strong correlation was observed between micro-structural measures from in vivo and micro-CT imaging, large shifts in their values for in vivo modalities warrant proper scanner calibration prior to adopting in multi-site and longitudinal studies.

  • A Multi-Organ Nucleus Segmentation Challenge
    Neeraj Kumar, Ruchika Verma, Deepak Anand, Yanning Zhou, Omer Fahri Onder, Efstratios Tsougenis, Hao Chen, Pheng-Ann Heng, Jiahui Li, Zhiqiang Hu, Yunzhi Wang, Navid Alemi Koohbanani, Mostafa Jahanifar, Neda Zamani Tajeddin, Ali Gooya, Nasir Rajpoot, Xuhua Ren, Sihang Zhou, Qian Wang, Dinggang Shen, Cheng-Kun Yang, Chi-Hung Weng, Wei-Hsiang Yu, Chao-Yuan Yeh, Shuang Yang, Shuoyu Xu, Pak Hei Yeung, Peng Sun, Amirreza Mahbod, Gerald Schaefer, Isabella Ellinger, Rupert Ecker, Orjan Smedby, Chunliang Wang, Benjamin Chidester, That-Vinh Ton, Minh-Triet Tran, Jian Ma, Minh N. Do, Simon Graham, Quoc Dang Vu, Jin Tae Kwak, Akshaykumar Gunda, Raviteja Chunduri, Corey Hu, Xiaoyang Zhou, Dariush Lotfi, Reza Safdari, Antanas Kascenas, Alison O'Neil, Dennis Eschweiler, Johannes Stegmaier, Yanping Cui, Baocai Yin, Kailin Chen, Xinmei Tian, Philipp Gruening, Erhardt Barth, Elad Arbel, Itay Remer, Amir Ben-Dor, Ekaterina Sirazitdinova, Matthias Kohl, Stefan Braunewell, Yuexiang Li, Xinpeng Xie, Linlin Shen, Jun Ma, Krishanu Das Baksi, Mohammad Azam Khan, Jaegul Choo, Adrian Colomer, Valery Naranjo, Linmin Pei, Khan M. Iftekharuddin, Kaushiki Roy, Debotosh Bhattacharjee, Anibal Pedraza, Maria Gloria Bueno, Sabarinathan Devanathan, Saravanan Radhakrishnan, Praveen Koduganty, Zihan Wu, Guanyu Cai, Xiaojie Liu, Yuqin Wang, and Amit Sethi

    IEEE Transactions on Medical Imaging, ISSN: 02780062, eISSN: 1558254X, Pages: 1380-1391, Published: May 2020 Institute of Electrical and Electronics Engineers (IEEE)

  • Shape Information Improves the Cross-Cohort Performance of Deep Learning-Based Segmentation of the Hippocampus
    Irene Brusini, Olof Lindberg, J-Sebastian Muehlboeck, Örjan Smedby, Eric Westman, and Chunliang Wang

    Frontiers in Neuroscience, ISSN: 16624548, eISSN: 1662453X, Published: 24 January 2020 Frontiers Media SA

  • Fully Bayesian longitudinal unsupervised learning for the assessment and visualization of AD heterogeneity and progression
    Konstantinos Poulakis, Daniel Ferreira, Joana B. Pereira, Örjan Smedby, Prashanthi Vemuri, and Eric Westman

    Aging, ISSN: 19454589, Pages: 12622-12647, Published: 2020 Impact Journals, LLC

  • Multimodal brain tumor segmentation with normal appearance autoencoder
    Mehdi Astaraki, Chunliang Wang, Gabriel Carrizo, Iuliana Toma-Dasu, and Örjan Smedby

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 03029743, eISSN: 16113349, Volume: 11993 LNCS, Pages: 316-323, Published: 2020 Springer International Publishing

  • Assessment of image quality in abdominal computed tomography: Effect of model-based iterative reconstruction, multi-planar reconstruction and slice thickness on potential dose reduction
    Bharti Kataria, Jonas Nilsson Althén, Örjan Smedby, Anders Persson, Hannibal Sökjer, and Michael Sandborg

    European Journal of Radiology, ISSN: 0720048X, eISSN: 18727727, Volume: 122, Published: January 2020 Elsevier BV

  • Comparison of acquisition protocols for ventilation/perfusion SPECT - A Monte Carlo study
    Maria Holstensson, Örjan Smedby, Gavin Poludniowski, Alejandro Sanchez-Crespo, Irina Savitcheva, Michael Öberg, Per Grybäck, Stefan Gabrielson, Patricia Sandqvist, Erika Bartholdson, and Rimma Axelsson

    Physics in Medicine and Biology, ISSN: 00319155, eISSN: 13616560, Published: 5 December 2019 IOP Publishing

  • Evaluation of algorithms for Multi-Modality Whole Heart Segmentation: An open-access grand challenge
    Xiahai Zhuang, Lei Li, Christian Payer, Darko Štern, Martin Urschler, Mattias P. Heinrich, Julien Oster, Chunliang Wang, Örjan Smedby, Cheng Bian, Xin Yang, Pheng-Ann Heng, Aliasghar Mortazi, Ulas Bagci, Guanyu Yang, Chenchen Sun, Gaetan Galisot, Jean-Yves Ramel, Thierry Brouard, Qianqian Tong, Weixin Si, Xiangyun Liao, Guodong Zeng, Zenglin Shi, Guoyan Zheng, Chengjia Wang, Tom MacGillivray, David Newby, Kawal Rhode, Sebastien Ourselin, Raad Mohiaddin, Jennifer Keegan, David Firmin, and Guang Yang

    Medical Image Analysis, ISSN: 13618415, eISSN: 13618423, Published: December 2019 Elsevier BV

  • Image quality and pathology assessment in CT Urography: When is the low-dose series sufficient?
    Bharti Kataria, Jonas Nilsson Althén, Örjan Smedby, Anders Persson, Hannibal Sökjer, and Michael Sandborg

    BMC Medical Imaging, eISSN: 14712342, Published: 9 August 2019 Springer Science and Business Media LLC

  • Early survival prediction in non-small cell lung cancer from PET/CT images using an intra-tumor partitioning method
    Mehdi Astaraki, Chunliang Wang, Giulia Buizza, Iuliana Toma-Dasu, Marta Lazzeroni, and Örjan Smedby

    Physica Medica, ISSN: 11201797, eISSN: 1724191X, Pages: 58-65, Published: April 2019 Elsevier BV

  • Normal Appearance Autoencoder for Lung Cancer Detection and Segmentation
    Mehdi Astaraki, Iuliana Toma-Dasu, Örjan Smedby, and Chunliang Wang

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 03029743, eISSN: 16113349, Volume: 11769 LNCS, Pages: 249-256, Published: 2019 Springer International Publishing

  • A Two-Stage U-Net Algorithm for Segmentation of Nuclei in H&E-Stained Tissues
    Amirreza Mahbod, Gerald Schaefer, Isabella Ellinger, Rupert Ecker, Örjan Smedby, and Chunliang Wang

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 03029743, eISSN: 16113349, Volume: 11435 LNCS, Pages: 75-82, Published: 2019 Springer International Publishing

  • Automatic rat brain segmentation from MRI using statistical shape models and random forest
    Simone Bendazzoli, Irene Brusini, Peter Damberg, Örjan Smedby, Leif Andersson, and Chunliang Wang

    Progress in Biomedical Optics and Imaging - Proceedings of SPIE, ISSN: 16057422, Volume: 10949, Published: 2019 SPIE

  • Simultaneous MR knee image segmentation and bias field correction using deep learning and partial convolution
    Fengkai Wan, Örjan Smedby, and Chunliang Wang

    Progress in Biomedical Optics and Imaging - Proceedings of SPIE, ISSN: 16057422, Volume: 10949, Published: 2019 SPIE

  • Voxel-Wise Clustering of Tractography Data for Building Atlases of Local Fiber Geometry
    Irene Brusini, Daniel Jörgens, Örjan Smedby, and Rodrigo Moreno

    Mathematics and Visualization, ISSN: 16123786, eISSN: 2197666X, Issue: 226249, Pages: 345-357, Published: 2019 Springer International Publishing

  • Pelvis segmentation using multi-pass U-Net and iterative shape estimation
    Chunliang Wang, Bryan Connolly, Pedro Filipe de Oliveira Lopes, Alejandro F. Frangi, and Örjan Smedby

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 03029743, eISSN: 16113349, Volume: 11404 LNCS, Pages: 49-57, Published: 2019 Springer International Publishing

  • Early tumor response prediction for lung cancer patients using novel longitudinal pattern features from sequential PET/CT image scans
    Giulia Buizza, Iuliana Toma-Dasu, Marta Lazzeroni, Chiara Paganelli, Marco Riboldi, Yongjun Chang, Örjan Smedby, and Chunliang Wang

    Physica Medica, ISSN: 11201797, eISSN: 1724191X, Pages: 21-29, Published: October 2018 Elsevier BV

  • Changes in brain architecture are consistent with altered fear processing in domestic rabbits
    Irene Brusini, Miguel Carneiro, Chunliang Wang, Carl-Johan Rubin, Henrik Ring, Sandra Afonso, José A. Blanco-Aguiar, Nuno Ferrand, Nima Rafati, Rafael Villafuerte, Örjan Smedby, Peter Damberg, Finn Hallböök, Mats Fredrikson, and Leif Andersson

    Proceedings of the National Academy of Sciences of the United States of America, ISSN: 00278424, eISSN: 10916490, Volume: 115, Pages: 7380-7385, Published: 10 July 2018 Proceedings of the National Academy of Sciences
    The most characteristic feature of domestic animals is their change in behavior associated with selection for tameness. Here we show, using high-resolution brain magnetic resonance imaging in wild and domestic rabbits, that domestication reduced amygdala volume and enlarged medial prefrontal cortex volume, supporting that areas driving fear have lost volume while areas modulating negative affect have gained volume during domestication. In contrast to the localized gray matter alterations, white matter anisotropy was reduced in the corona radiata, corpus callosum, and the subcortical white matter. This suggests a compromised white matter structural integrity in projection and association fibers affecting both afferent and efferent neural flow, consistent with reduced neural processing. We propose that compared with their wild ancestors, domestic rabbits are less fearful and have an attenuated flight response because of these changes in brain architecture.

  • Direct estimation of human trabecular bone stiffness using cone beam computed tomography
    Eva Klintström, Benjamin Klintström, Dieter Pahr, Torkel B. Brismar, Örjan Smedby, and Rodrigo Moreno

    Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology, ISSN: 22124403, Volume: 126, Pages: 72-82, Published: July 2018 Elsevier BV

RECENT SCHOLAR PUBLICATIONS

  • Tumor Detection in PET/CT Using Multimodal Image Inpainting
    M Astaraki, F De Benetti, I Toma-Dasu, Smedby, C Wang, N Navab, ...
    Nuklearmedizin-NuclearMedicine 61 (02), V24 2022

  • MRI-derived brain age as a biomarker of ageing in rats: validation using a healthy lifestyle intervention
    I Brusini, E MacNicol, E Kim, Smedby, C Wang, E Westman, ...
    Neurobiology of aging 109, 204-215 2022

  • A Comparative Study of Radiomics and Deep-Learning Based Methods for Pulmonary Nodule Malignancy Prediction in Low Dose CT Images.
    M Astaraki, G Yang, Y Zakko, I Toma-Dasu, Smedby, C Wang
    Frontiers in oncology 11, 737368-737368 2021

  • Image Quality and Potential Dose Reduction Using Advanced Modeled Iterative Reconstruction (Admire) in Abdominal Ct-A Review
    B Kataria, J Nilsson Althen, Smedby, A Persson, H Skjer, M Sandborg
    Radiation Protection Dosimetry 195 (3-4), 177-187 2021

  • Deriving brain imaging biomarkers with deep learning
    Smedby
    Emerging Topics in Artificial Intelligence (ETAI) 2021 11804, 118040Q 2021

  • Benign-malignant pulmonary nodule classification in low-dose CT with convolutional features
    M Astaraki, Y Zakko, IT Dasu, Smedby, C Wang
    Physica Medica 83, 146-153 2021

  • Development and evaluation of a 3D annotation software for interactive COVID-19 lesion segmentation in chest CT
    S Bendazzoli, I Brusini, M Astaraki, M Persson, J Yu, B Connolly, S Nyrn, ...
    arXiv preprint arXiv:2012.14752 2020

  • A comparative study of trabecular bone micro-structural measurements using different CT modalities
    I Guha, B Klintstrm, E Klintstrm, X Zhang, Smedby, R Moreno, ...
    Physics in Medicine & Biology 65 (23), 235029 2020

  • Quantitative MRI using relaxometry in malignant gliomas detects contrast enhancement in peritumoral oedema
    I Blystad, JBM Warntjes, Smedby, P Lundberg, EM Larsson, A Tisell
    Scientific reports 10 (1), 1-9 2020

  • Fully Bayesian longitudinal unsupervised learning for the assessment and visualization of AD heterogeneity and progression
    K Poulakis, D Ferreira, JB Pereira, Smedby, P Vemuri, E Westman
    Aging (Albany NY) 12 (13), 12622 2020

  • A deep learning-based pipeline for error detection and quality control of brain MRI segmentation results
    I Brusini, DF Padilla, J Barroso, I Skoog, Smedby, E Westman, C Wang
    arXiv preprint arXiv:2005.13987 2020

  • Shape information improves the cross-cohort performance of deep learning-based segmentation of the hippocampus
    I Brusini, O Lindberg, JS Muehlboeck, Smedby, E Westman, C Wang
    Frontiers in neuroscience 14, 15 2020

  • Assessment of image quality in abdominal computed tomography: effect of model-based iterative reconstruction, multi-planar reconstruction and slice thickness on potential dose
    B Kataria, JN Althn, Smedby, A Persson, H Skjer, M Sandborg
    European Journal of Radiology 122, 108703 2020

  • Comparison of acquisition protocols for ventilation/perfusion SPECT—a Monte Carlo study
    M Holstensson, Smedby, G Poludniowski, A Sanchez-Crespo, ...
    Physics in Medicine & Biology 64 (23), 235018 2019

  • Image quality and pathology assessment in CT Urography: when is the low-dose series sufficient?
    B Kataria, J Nilsson Althn, Smedby, A Persson, H Skjer, M Sandborg
    BMC medical imaging 19 (1), 1-9 2019

  • Evaluation of algorithms for multi-modality whole heart segmentation: an open-access grand challenge
    X Zhuang, L Li, C Payer, D Štern, M Urschler, MP Heinrich, J Oster, ...
    Medical image analysis 58, 101537 2019

  • A multi-organ nucleus segmentation challenge
    N Kumar, R Verma, D Anand, Y Zhou, OF Onder, E Tsougenis, H Chen, ...
    IEEE transactions on medical imaging 39 (5), 1380-1391 2019

  • Multimodal brain tumor segmentation with normal appearance autoencoder
    M Astaraki, C Wang, G Carrizo, I Toma-Dasu, Smedby
    International MICCAI Brainlesion Workshop, 316-323 2019

  • Normal appearance autoencoder for lung cancer detection and segmentation
    M Astaraki, I Toma-Dasu, Smedby, C Wang
    International Conference on Medical Image Computing and Computer-Assisted 2019

  • Voxel-wise clustering of tractography data for building atlases of local fiber geometry
    I Brusini, D Jrgens, Smedby, R Moreno
    International Conference on Medical Image Computing and Computer-Assisted 2019

MOST CITED SCHOLAR PUBLICATIONS

  • Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge
    S Bakas, M Reyes, A Jakab, S Bauer, M Rempfler, A Crimi, RT Shinohara, ...
    arXiv preprint arXiv:1811.02629 2018
    Citations: 856

  • Standardized evaluation methodology and reference database for evaluating coronary artery centerline extraction algorithms
    M Schaap, CT Metz, T van Walsum, AG van der Giessen, AC Weustink, ...
    Medical image analysis 13 (5), 701-714 2009
    Citations: 373

  • Web‐based interactive 3D visualization as a tool for improved anatomy learning
    H Petersson, D Sinkvist, C Wang, Smedby
    Anatomical sciences education 2 (2), 61-68 2009
    Citations: 263

  • MRBrainS challenge: online evaluation framework for brain image segmentation in 3T MRI scans
    AM Mendrik, KL Vincken, HJ Kuijf, M Breeuwer, WH Bouvy, J De Bresser, ...
    Computational intelligence and neuroscience 2015 2015
    Citations: 209

  • Iodinated contrast opacification gradients in normal coronary arteries imaged with prospectively ECG-gated single heart beat 320-detector row computed tomography
    ML Steigner, D Mitsouras, AG Whitmore, HJ Otero, C Wang, O Buckley, ...
    Circulation: Cardiovascular Imaging 3 (2), 179-186 2010
    Citations: 171

  • Advanced 3D visualization in student-centred medical education
    C Siln, S Wirell, J Kvist, E Nylander, Smedby
    Medical teacher 30 (5), e115-e124 2008
    Citations: 146

  • Evaluation of algorithms for multi-modality whole heart segmentation: an open-access grand challenge
    X Zhuang, L Li, C Payer, D Štern, M Urschler, MP Heinrich, J Oster, ...
    Medical image analysis 58, 101537 2019
    Citations: 136

  • A novel infraclavicular brachial plexus block: the lateral and sagittal technique, developed by magnetic resonance imaging studies
    Klaastad, HJ Smith, Smedby, EH Winther-Larssen, P Brodal, ...
    Anesthesia & Analgesia 98 (1), 252-256 2004
    Citations: 127

  • A multi-organ nucleus segmentation challenge
    N Kumar, R Verma, D Anand, Y Zhou, OF Onder, E Tsougenis, H Chen, ...
    IEEE transactions on medical imaging 39 (5), 1380-1391 2019
    Citations: 118

  • Contrast-enhanced magnetic resonance cholangiography with Gd-BOPTA and Gd-EOB-DTPA in healthy subjects
    N Dahlstrm, A Persson, N Albiin, Smedby, TB Brismar
    Acta Radiologica 48 (4), 362-368 2007
    Citations: 118

  • Two-dimensional tortuosity of the superficial femoral artery in early atherosclerosis
    Smedby, N Hgman, S Nilsson, U Erikson, AG Olsson, G Walldius
    Journal of vascular research 30 (4), 181-191 1993
    Citations: 103

  • Synthetic MRI of the brain in a clinical setting
    I Blystad, JBM Warntjes, O Smedby, AM Landtblom, P Lundberg, ...
    Acta radiologica 53 (10), 1158-1163 2012
    Citations: 102

  • Quantifying differences in hepatic uptake of the liver specific contrast agents Gd-EOB-DTPA and Gd-BOPTA: a pilot study
    O Dahlqvist Leinhard, N Dahlstrm, J Kihlberg, P Sandstrm, TB Brismar, ...
    European radiology 22 (3), 642-653 2012
    Citations: 102

  • Distribution of local anesthetic in axillary brachial plexus block: a clinical and magnetic resonance imaging study
    Klaastad, Smedby, GE Thompson, T Tillung, P Kristian Hol, ...
    The Journal of the American Society of Anesthesiologists 96 (6), 1315-1324 2002
    Citations: 99

  • Visual grading regression: analysing data from visual grading experiments with regression models
    Smedby, M Fredrikson
    The British journal of radiology 83 (993), 767-775 2010
    Citations: 90

  • Quantitative abdominal fat estimation using MRI
    OD Leinhard, A Johansson, J Rydell, O Smedby, F Nystrom, P Lundberg, ...
    2008 19th international conference on pattern recognition, 1-4 2008
    Citations: 89

  • Tortuosity and atherosclerosis in the femoral artery: what is cause and what is effect?
    Smedby, L Bergstrand
    Annals of biomedical engineering 24 (4), 474-480 1996
    Citations: 87

  • Do plaques grow upstream or downstream? An angiographic study in the femoral artery
    O Smedby
    Arteriosclerosis, thrombosis, and vascular biology 17 (5), 912-918 1997
    Citations: 83

  • Standardized evaluation system for left ventricular segmentation algorithms in 3D echocardiography
    O Bernard, JG Bosch, B Heyde, M Alessandrini, D Barbosa, ...
    IEEE transactions on medical imaging 35 (4), 967-977 2015
    Citations: 81

  • Liver vessel enhancement by Gd-BOPTA and Gd-EOB-DTPA: a comparison in healthy volunteers
    TB Brismar, N Dahlstrm, N Edsborg, A Persson, Smedby, N Albiin
    Acta radiologica 50 (7), 709-715 2009
    Citations: 78