Örjan Smedby

@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

192

Scopus Publications

10221

Scholar Citations

48

Scholar h-index

133

Scholar i10-index

Scopus Publications

  • Efficient Generation of Synthetic Breast CT Slices By Combining Generative and Super-Resolution Models
    Zhikai Yang, Mehdi Astaraki, Örjan Smedby, and Rodrigo Moreno
    Springer Nature Switzerland

  • Correction to: Multi-cohort and longitudinal Bayesian clustering study of stage and subtype in Alzheimer’s disease (Nature Communications, (2022), 13, 1, (4566), 10.1038/s41467-022-32202-6)
    Konstantinos Poulakis, Joana B. Pereira, J.-Sebastian Muehlboeck, Lars-Olof Wahlund, Örjan Smedby, Giovanni Volpe, Colin L. Masters, David Ames, Yoshiki Niimi, Takeshi Iwatsubo,et al.
    Springer Science and Business Media LLC

  • Automated region growing-based segmentation for trabecular bone structure in fresh-frozen human wrist specimens
    Eva Klintström, Benjamin Klintström, Örjan Smedby, and Rodrigo Moreno
    Springer Science and Business Media LLC
    AbstractBone strength depends on both mineral content and bone structure. Measurements of bone microstructure on specimens can be performed by micro-CT. In vivo measurements are reliably performed by high-resolution peripheral computed tomography (HR-pQCT) using dedicated software. In previous studies from our research group, trabecular bone properties on CT data of defatted specimens from many different CT devices have been analyzed using an Automated Region Growing (ARG) algorithm-based code, showing strong correlations to micro-CT.The aim of the study was to validate the possibility of segmenting and measuring trabecular bone structure from clinical CT data of fresh-frozen human wrist specimens. Data from micro-CT was used as reference. The hypothesis was that the ARG-based in-house built software could be used for such measurements.HR-pQCT image data at two resolutions (61 and 82 µm isotropic voxels) from 23 fresh-frozen human forearms were analyzed. Correlations to micro-CT were strong, varying from 0.72 to 0.99 for all parameters except trabecular termini and nodes. The bone volume fraction had correlations varying from 0.95 to 0.98 but was overestimated compared to micro-CT, especially at the lower resolution. Trabecular separation and spacing were the most stable parameters with correlations at 0.80-0.97 and mean values in the same range as micro-CT.Results from this in vitro study show that an ARG-based software could be used for segmenting and measuring 3D trabecular bone structure from clinical CT data of fresh-frozen human wrist specimens using micro-CT data as reference. Over-and underestimation of several of the bone structure parameters must however be taken into account.

  • Image quality assessments in abdominal CT: Relative importance of dose, iterative reconstruction strength and slice thickness
    B. Kataria, M. Woisetschläger, J. Nilsson Althén, M. Sandborg, and Ö. Smedby
    Elsevier BV

  • Acquisition Duration Optimization Using Visual Grading Regression in [<sup>68</sup>Ga]FAPI-46 PET Imaging of Oncologic Patients
    Ted Nilsson, Pawel Rasinski, Örjan Smedby, Siri af Burén, Ernesto Sparrelid, J. Matthias Löhr, Thuy A. Tran, August Blomgren, Antonios Tzortzakakis, Rimma Axelsson,et al.
    Society of Nuclear Medicine
    Fibroblast activation protein is a promising target for oncologic molecular imaging with radiolabeled fibroblast activation protein inhibitors (FAPI) in a large variety of cancers. However, there are yet no published recommendations on how to set up an optimal imaging protocol for FAPI PET/CT. It is important to optimize the acquisition duration and strive toward an acquisition that is sufficiently short while simultaneously providing sufficient image quality to ensure a reliable diagnosis. The aim of this study was to evaluate the feasibility of reducing the acquisition duration of [68Ga]FAPI-46 imaging while maintaining satisfactory image quality, with certainty that the radiologist's ability to make a clinical diagnosis would not be affected. Methods: [68Ga]FAPI-46 PET/CT imaging was performed on 10 patients scheduled for surgical resection of suspected pancreatic cancer, 60 min after administration of 3.6 ± 0.2 MBq/kg. The acquisition time was 4 min/bed position, and the raw PET data were statistically truncated and reconstructed to represent images with an acquisition duration of 1, 2, and 3 min/bed position, additional to the reference images of 4 min/bed position. Four image quality criteria that focused on the ability to distinguish specific anatomic details, as well as perceived image noise and overall image quality, were scored on a 4-point Likert scale and analyzed with mixed-effects ordinal logistic regression. Results: A trend toward increasing image quality scores with increasing acquisition duration was observed for all criteria. For the overall image quality, there was no significant difference between 3 and 4 min/bed position, whereas 1 and 2 min/bed position were rated significantly (P < 0.05) lower than 4 min/bed position. For the other criteria, all images with a reduced acquisition duration were rated significantly inferior to images obtained at 4 min/bed position. Conclusion: The acquisition duration can be reduced from 4 to 3 min/bed position while maintaining satisfactory image quality. Reducing the acquisition duration to 2 min/bed position or lower is not recommended since it results in inferior-quality images so noisy that clinical interpretation is significantly disrupted.

  • Lung vessel connectivity map as anatomical prior knowledge for deep learning-based lung lobe segmentation
    Simone Bendazzoli, Emelie Bäcklin, Örjan Smedby, Birgitta Janerot-Sjoberg, Bryan Connolly, and Chunliang Wang
    SPIE-Intl Soc Optical Eng
    Purpose Our study investigates the potential benefits of incorporating prior anatomical knowledge into a deep learning (DL) method designed for the automated segmentation of lung lobes in chest CT scans. Approach We introduce an automated DL-based approach that leverages anatomical information from the lung's vascular system to guide and enhance the segmentation process. This involves utilizing a lung vessel connectivity (LVC) map, which encodes relevant lung vessel anatomical data. Our study explores the performance of three different neural network architectures within the nnU-Net framework: a standalone U-Net, a multitasking U-Net, and a cascade U-Net. Results Experimental findings suggest that the inclusion of LVC information in the DL model can lead to improved segmentation accuracy, particularly, in the challenging boundary regions of expiration chest CT volumes. Furthermore, our study demonstrates the potential for LVC to enhance the model's generalization capabilities. Finally, the method's robustness is evaluated through the segmentation of lung lobes in 10 cases of COVID-19, demonstrating its applicability in the presence of pulmonary diseases. Conclusions Incorporating prior anatomical information, such as LVC, into the DL model shows promise for enhancing segmentation performance, particularly in the boundary regions. However, the extent of this improvement has limitations, prompting further exploration of its practical applicability.

  • Pulmonary volumes and signs of chronic airflow limitation in quantitative computed tomography
    Emelie Bäcklin, Adrian Gonon, Magnus Sköld, Örjan Smedby, Eva Breznik, and Birgitta Janerot‐Sjoberg
    Wiley
    AbstractBackgroundComputed tomography (CT) offers pulmonary volumetric quantification but is not commonly used in healthy individuals due to radiation concerns. Chronic airflow limitation (CAL) is one of the diagnostic criteria for chronic obstructive pulmonary disease (COPD), where early diagnosis is important. Our aim was to present reference values for chest CT volumetric and radiodensity measurements and explore their potential in detecting early signs of CAL.MethodsFrom the population‐based Swedish CArdioPulmonarybioImage Study (SCAPIS), 294 participants aged 50–64, were categorized into non‐CAL (n = 258) and CAL (n = 36) groups based on spirometry. From inspiratory and expiratory CT images we compared lung volumes, mean lung density (MLD), percentage of low attenuation volume (LAV%) and LAV cluster volume between groups, and against reference values from static pulmonary function test (PFT).ResultsThe CAL group exhibited larger lung volumes, higher LAV%, increased LAV cluster volume and lower MLD compared to the non‐CAL group. Lung volumes significantly deviated from PFT values. Expiratory measurements yielded more reliable results for identifying CAL compared to inspiratory. Using a cut‐off value of 0.6 for expiratory LAV%, we achieved sensitivity, specificity and positive/negative predictive values of 72%, 85% and 40%/96%, respectively.ConclusionWe present volumetric reference values from inspiratory and expiratory chest CT images for a middle‐aged healthy cohort. These results are not directly comparable to those from PFTs. Measures of MLD and LAV can be valuable in the evaluation of suspected CAL. Further validation and refinement are necessary to demonstrate its potential as a decision support tool for early detection of COPD.

  • Image quality in CT thorax: effect of altering reconstruction algorithm and tube load
    Bharti Kataria, Mischa Woisetschläger, Jonas Nilsson Althén, Michael Sandborg, and Örjan Smedby
    Oxford University Press (OUP)
    Abstract Non-linear properties of iterative reconstruction (IR) algorithms can alter image texture. We evaluated the effect of a model-based IR algorithm (advanced modelled iterative reconstruction; ADMIRE) and dose on computed tomography thorax image quality. Dual-source scanner data were acquired at 20, 45 and 65 reference mAs in 20 patients. Images reconstructed with filtered back projection (FBP) and ADMIRE Strengths 3–5 were assessed independently by six radiologists and analysed using an ordinal logistic regression model. For all image criteria studied, the effects of tube load 20 mAs and all ADMIRE strengths were significant (p &amp;lt; 0.001) when compared to reference categories 65 mAs and FBP. Increase in tube load from 45 to 65 mAs showed image quality improvement in three of six criteria. Replacing FBP with ADMIRE significantly improves perceived image quality for all criteria studied, potentially permitting a dose reduction of almost 70% without loss in image quality.

  • Automatic Segmentation of Bone Graft in Maxillary Sinus Via Distance Constrained Network Guided by Prior Anatomical Knowledge
    Jiangchang Xu, Jie Gao, Shuanglin Jiang, Chunliang Wang, Örjan Smedby, Yiqun Wu, Xiaoyi Jiang, and Xiaojun Chen
    Institute of Electrical and Electronics Engineers (IEEE)

  • 3D Breast Ultrasound Image Classification Using 2.5D Deep learning
    Zhikai Yang, Tianyu Fan, Örjan Smedby, and Rodrigo Moreno
    SPIE
    The 3D breast ultrasound is a radiation-free and effective imaging technology for breast tumor diagnosis. However, checking the 3D breast ultrasound is time-consuming compared to mammograms. To reduce the workload of radiologists, we proposed a 2.5D deep learning-based breast ultrasound tumor classification system. First, we used the pre-trained STU-Net to finetune and segment the tumor in 3D. Then, we fine-tuned the DenseNet-121 for classification using the 10 slices with the biggest tumoral area and their adjacent slices. The Tumor Detection, Segmentation, and Classification on Automated 3D Breast Ultrasound (TDSC-ABUS) MICCAI Challenge 2023 dataset was used to train and validate the performance of the proposed method. Compared to a 3D convolutional neural network model and radiomics, our proposed method has better performance.

  • Using simulated breast lesions based on Perlin noise for evaluation of lesion segmentation
    Hanna Tomic, Zhikai Yang, Anders Tingberg, Sophia Zackrisson, Rodrigo Moreno, Örjan Smedby, Magnus Dustler, and Predrag Bakic
    SPIE


  • Validation of automated post-adjustments of HDR prostate brachytherapy treatment plans by quantitative measures and oncologist observer study
    Frida Dohlmar, Björn Morén, Michael Sandborg, Örjan Smedby, Alexander Valdman, Torbjörn Larsson, and Åsa Carlsson Tedgren
    Elsevier BV

  • Synthesis of Pediatric Brain Tumor Images With Mass Effect
    Yu Zhou, Jingru Fu, Örjan Smedby, and Rodrigo Moreno
    SPIE
    In children, brain tumors are the leading cause of cancer-related death. The amount of labeled data in children is much lower than that for adult subjects. This paper proposes a new method to synthesize high-quality pathological pediatric MRI brain images from pathological adult ones. To realistically simulate the appearance of brain tumors, the proposed method considers the mass effect, i.e., the deformation induced by the tumor to the surrounding tissue. First, a probabilistic U-Net was trained to predict a deformation field that encodes the mass effect from the healthy-pathological image pair. Second, the learned deformation field was utilized to warp the healthy mask to simulate the mass effect. The tumor mask is also added to the warped mask. Finally, a label-to-image transformer, i.e., the SPADE GAN, was trained to synthesize a pathological image from the segmentation masks of gray matter, white matter, CSF and the tumor. The synthetic images were evaluated in two quantitative ways: i) three supervised segmentation pipelines were trained on datasets with and without synthetic images. Two pipelines show over 1% improvements in the Dice scores when the datasets were augmented with synthetic data. ii) The Fr´echet inception distance was measured between real and synthetic image distributions. Results show that SPADE outperforms the state-of-the-art Pix2PixHD method in both T1w and T2w modalities. The source code can be accessed on https://github.com/audreyeternal/pediatric-tumor-generation


  • Multi-cohort and longitudinal Bayesian clustering study of stage and subtype in Alzheimer’s disease
    Konstantinos Poulakis, Joana B. Pereira, J.-Sebastian Muehlboeck, Lars-Olof Wahlund, Örjan Smedby, Giovanni Volpe, Colin L. Masters, David Ames, Yoshiki Niimi, Takeshi Iwatsubo,et al.
    Springer Science and Business Media LLC
    AbstractUnderstanding Alzheimer’s disease (AD) heterogeneity is important for understanding the underlying pathophysiological mechanisms of AD. However, AD atrophy subtypes may reflect different disease stages or biologically distinct subtypes. Here we use longitudinal magnetic resonance imaging data (891 participants with AD dementia, 305 healthy control participants) from four international cohorts, and longitudinal clustering to estimate differential atrophy trajectories from the age of clinical disease onset. Our findings (in amyloid-β positive AD patients) show five distinct longitudinal patterns of atrophy with different demographical and cognitive characteristics. Some previously reported atrophy subtypes may reflect disease stages rather than distinct subtypes. The heterogeneity in atrophy rates and cognitive decline within the five longitudinal atrophy patterns, potentially expresses a complex combination of protective/risk factors and concomitant non-AD pathologies. By alternating between the cross-sectional and longitudinal understanding of AD subtypes these analyses may allow better understanding of disease heterogeneity.

  • Photon-counting detector CT and energy-integrating detector CT for trabecular bone microstructure analysis of cubic specimens from human radius
    Benjamin Klintström, Lilian Henriksson, Rodrigo Moreno, Alexandr Malusek, Örjan Smedby, Mischa Woisetschläger, and Eva Klintström
    Springer Science and Business Media LLC
    Abstract Background As bone microstructure is known to impact bone strength, the aim of this in vitro study was to evaluate if the emerging photon-counting detector computed tomography (PCD-CT) technique may be used for measurements of trabecular bone structures like thickness, separation, nodes, spacing and bone volume fraction. Methods Fourteen cubic sections of human radius were scanned with two multislice CT devices, one PCD-CT and one energy-integrating detector CT (EID-CT), using micro-CT as a reference standard. The protocols for PCD-CT and EID-CT were those recommended for inner- and middle-ear structures, although at higher mAs values: PCD-CT at 450 mAs and EID-CT at 600 (dose equivalent to PCD-CT) and 1000 mAs. Average measurements of the five bone parameters as well as dispersion measurements of thickness, separation and spacing were calculated using a three-dimensional automated region growing (ARG) algorithm. Spearman correlations with micro-CT were computed. Results Correlations with micro-CT, for PCD-CT and EID-CT, ranged from 0.64 to 0.98 for all parameters except for dispersion of thickness, which did not show a significant correlation (p = 0.078 to 0.892). PCD-CT had seven of the eight parameters with correlations ρ &gt; 0.7 and three ρ &gt; 0.9. The dose-equivalent EID-CT instead had four parameters with correlations ρ &gt; 0.7 and only one ρ &gt; 0.9. Conclusions In this in vitro study of radius specimens, strong correlations were found between trabecular bone structure parameters computed from PCD-CT data when compared to micro-CT. This suggests that PCD-CT might be useful for analysing bone microstructure in the peripheral human skeleton.

  • A review on AI-based medical image computing in head and neck surgery
    Jiangchang Xu, Bolun Zeng, Jan Egger, Chunliang Wang, Örjan Smedby, Xiaoyi Jiang, and Xiaojun Chen
    IOP Publishing
    Abstract Head and neck surgery is a fine surgical procedure with a complex anatomical space, difficult operation and high risk. Medical image computing (MIC) that enables accurate and reliable preoperative planning is often needed to reduce the operational difficulty of surgery and to improve patient survival. At present, artificial intelligence, especially deep learning, has become an intense focus of research in MIC. In this study, the application of deep learning-based MIC in head and neck surgery is reviewed. Relevant literature was retrieved on the Web of Science database from January 2015 to May 2022, and some papers were selected for review from mainstream journals and conferences, such as IEEE Transactions on Medical Imaging, Medical Image Analysis, Physics in Medicine and Biology, Medical Physics, MICCAI, etc. Among them, 65 references are on automatic segmentation, 15 references on automatic landmark detection, and eight references on automatic registration. In the elaboration of the review, first, an overview of deep learning in MIC is presented. Then, the application of deep learning methods is systematically summarized according to the clinical needs, and generalized into segmentation, landmark detection and registration of head and neck medical images. In segmentation, it is mainly focused on the automatic segmentation of high-risk organs, head and neck tumors, skull structure and teeth, including the analysis of their advantages, differences and shortcomings. In landmark detection, the focus is mainly on the introduction of landmark detection in cephalometric and craniomaxillofacial images, and the analysis of their advantages and disadvantages. In registration, deep learning networks for multimodal image registration of the head and neck are presented. Finally, their shortcomings and future development directions are systematically discussed. The study aims to serve as a reference and guidance for researchers, engineers or doctors engaged in medical image analysis of head and neck surgery.

  • Prior-aware autoencoders for lung pathology segmentation
    Mehdi Astaraki, Örjan Smedby, and Chunliang Wang
    Elsevier BV

  • Spherical Convolutional Neural Networks for Survival Rate Prediction in Cancer Patients
    Fabian Sinzinger, Mehdi Astaraki, Örjan Smedby, and Rodrigo Moreno
    Frontiers Media SA
    ObjectiveSurvival Rate Prediction (SRP) is a valuable tool to assist in the clinical diagnosis and treatment planning of lung cancer patients. In recent years, deep learning (DL) based methods have shown great potential in medical image processing in general and SRP in particular. This study proposes a fully-automated method for SRP from computed tomography (CT) images, which combines an automatic segmentation of the tumor and a DL-based method for extracting rotational-invariant features.MethodsIn the first stage, the tumor is segmented from the CT image of the lungs. Here, we use a deep-learning-based method that entails a variational autoencoder to provide more information to a U-Net segmentation model. Next, the 3D volumetric image of the tumor is projected onto 2D spherical maps. These spherical maps serve as inputs for a spherical convolutional neural network that approximates the log risk for a generalized Cox proportional hazard model.ResultsThe proposed method is compared with 17 baseline methods that combine different feature sets and prediction models using three publicly-available datasets: Lung1 (n=422), Lung3 (n=89), and H&amp;amp;N1 (n=136). We observed comparable C-index scores compared to the best-performing baseline methods in a 5-fold cross-validation on Lung1 (0.59 ± 0.03 vs. 0.62 ± 0.04). In comparison, it slightly outperforms all methods in inter-data set evaluation (0.64 vs. 0.63). The best-performing method from the first experiment reduced its performance to 0.61 and 0.62 for Lung3 and H&amp;amp;N1, respectively.DiscussionThe experiments suggest that the performance of spherical features is comparable with previous approaches, but they generalize better when applied to unseen datasets. That might imply that orientation-independent shape features are relevant for SRP. The performance of the proposed method was very similar, using manual and automatic segmentation methods. This makes the proposed model useful in cases where expert annotations are not available or difficult to obtain.

  • 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
    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 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
    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
    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
    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.

RECENT SCHOLAR PUBLICATIONS

  • Automatic Segmentation of Bone Graft in Maxillary Sinus Via Distance Constrained Network Guided by Prior Anatomical Knowledge
    J Xu, J Gao, S Jiang, C Wang, Smedby, Y Wu, X Jiang, X Chen
    IEEE Journal of Biomedical and Health Informatics 2024

  • Efficient Generation of Synthetic Breast CT Slices By Combining Generative and Super-Resolution Models
    Z Yang, M Astaraki, Smedby, R Moreno
    Deep Breast Workshop on AI and Imaging for Diagnostic and Treatment 2024

  • Image quality assessments in abdominal CT: Relative importance of dose, iterative reconstruction strength and slice thickness
    B Kataria, M Woisetschlger, JN Althn, M Sandborg, Smedby
    Radiography 30 (6), 1563-1571 2024

  • Acquisition Duration Optimization Using Visual Grading Regression in [68Ga] FAPI-46 PET Imaging of Oncologic Patients
    T Nilsson, P Rasinski, Smedby, S Af Burn, E Sparrelid, JM Lhr, ...
    Journal of Nuclear Medicine Technology 52 (3), 221-228 2024

  • Author Correction: Multi-cohort and longitudinal Bayesian clustering study of stage and subtype in Alzheimer’s disease
    K Poulakis, JB Pereira, JS Muehlboeck, LO Wahlund, Smedby, G Volpe, ...
    nature communications 15 (1), 5784 2024

  • Lung vessel connectivity map as anatomical prior knowledge for deep learning-based lung lobe segmentation
    S Bendazzoli, E Bcklin, Smedby, B Janerot-Sjoberg, B Connolly, ...
    Journal of Medical Imaging 11 (4), 044001-044001 2024

  • Pulmonary volumes and signs of chronic airflow limitation in quantitative computed tomography
    E Bcklin, A Gonon, M Skld, Smedby, E Breznik, B Janerot‐Sjoberg
    Clinical Physiology and Functional Imaging 44 (4), 340-348 2024

  • Unsupervised domain adaptation for pediatric brain tumor segmentation
    J Fu, S Bendazzoli, Smedby, R Moreno
    arXiv preprint arXiv:2406.16848 2024

  • 3D breast ultrasound image classification using 2.5 D deep learning
    Z Yang, T Fan, Smedby, R Moreno
    17th International Workshop on Breast Imaging (IWBI 2024) 13174, 443-449 2024

  • Automated region growing-based segmentation for trabecular bone structure in fresh-frozen human wrist specimens
    E Klintstrm, B Klintstrm, Smedby, R Moreno
    BMC Medical Imaging 24 (1), 101 2024

  • Lesion localization in digital breast tomosynthesis with deformable transformers by using 2.5 d information
    Z Yang, T Fan, Smedby, R Moreno
    Medical Imaging 2024: Computer-Aided Diagnosis 12927, 85-90 2024

  • Using simulated breast lesions based on Perlin noise for evaluation of lesion segmentation
    H Tomic, Z Yang, A Tingberg, S Zackrisson, R Moreno, Smedby, ...
    Medical Imaging 2024: Physics of Medical Imaging 12925, 279-288 2024

  • Image quality in CT thorax: effect of altering reconstruction algorithm and tube load
    B Kataria, M Woisetschlger, J Nilsson Althn, M Sandborg, Smedby
    Radiation Protection Dosimetry 200 (5), 504-514 2024

  • User menu
    N Hara, M Onoguchi, H Kawaguchi, N Matsushima, O Houjou, M Murai, ...
    Change 2024

  • A deformation-based morphometry framework for disentangling Alzheimer's disease from normal aging using learned normal aging templates
    J Fu, D Ferreira, Smedby, R Moreno
    arXiv preprint arXiv:2311.08176 2023

  • Autopaint: A self-inpainting method for unsupervised anomaly detection
    M Astaraki, F De Benetti, Y Yeganeh, I Toma-Dasu, Smedby, C Wang, ...
    arXiv preprint arXiv:2305.12358 2023

  • Validation of automated post-adjustments of HDR prostate brachytherapy treatment plans by quantitative measures and oncologist observer study
    F Dohlmar, B Morn, M Sandborg, Smedby, A Valdman, T Larsson, ...
    Brachytherapy 22 (3), 407-415 2023

  • Synthesis of pediatric brain tumor images with mass effect
    Y Zhou, J Fu, Smedby, R Moreno
    Medical imaging 2023: Image processing 12464, 712-720 2023

  • A dose optimization study using Visual Grading Regression in [68Ga]-FAPI-46 PET imaging of patients with pancreatic lesions
    T Nilsson, P Rasinski, S af Buren, Smedby, A Blomgren, M Lohr, ...
    European Journal of Nuclear Medicine and Molecular Imaging 50 (SUPPL 1 2023

  • Learning effects in visual grading assessment of model-based reconstruction algorithms in abdominal Computed Tomography
    B Kataria, J man, M Sandborg, Smedby
    European Journal of Radiology Open 10, 100490 2023

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: 2299

  • 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: 487

  • 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: 436

  • 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: 324

  • 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: 307

  • 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 (1), 813696 2015
    Citations: 291

  • 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: 199

  • 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: 193

  • 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: 174

  • 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: 142

  • 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: 128

  • 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: 122

  • 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, 642-653 2012
    Citations: 117

  • 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: 115

  • 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: 111

  • Quantitative MRI for analysis of peritumoral edema in malignant gliomas
    I Blystad, JBM Warntjes, Smedby, P Lundberg, EM Larsson, A Tisell
    PLoS One 12 (5), e0177135 2017
    Citations: 109

  • 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: 108

  • Distribution of local anesthetic in axillary brachial plexus block: a clinical and magnetic resonance imaging study
     Klaastad, Smedby, GE Thompson, T Tillung, PK Hol, JS Rtnes, ...
    Anesthesiology 96 (6), 1315-1324 2002
    Citations: 101

  • Tortuosity and atherosclerosis in the femoral artery: what is cause and what is effect?
     Smedby, L Bergstrand
    Annals of biomedical engineering 24, 474-480 1996
    Citations: 101

  • 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: 98