| 1. |
|
|
| 2. |
- Langner, Taro
(författare)
-
Deep Regression and Segmentation for Medical Inference from Large-Scale Magnetic Resonance Imaging
- 2021
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Large-scale studies, such as UK Biobank, acquire medical imaging data for thousands of participants. With magnetic resonance imaging (MRI), comprehensive representations of human anatomy can be provided for non-invasive assessments of health-related conditions, body composition, organ volumes, and more. The sheer quantity of resulting image data itself poses a challenge, however, as manual processing and evaluation at the given scale is typically no longer feasible. For automated image analysis, machine learning techniques involving deep learning with convolutional neural networks have established state-of-the-art results in recent years. These systems can perform a multitude of tasks on medical image data, such as predicting measurements, classifying certain conditions, and enhancing image quality. The overall aim of this thesis was to explore the potential of deep learning for automated analysis of large-scale MRI derived from several studies.Fully-convolutional networks for semantic segmentation were adapted and evaluated for the automated delineation and quantification of adipose tissue depots and abdominal organs.As an alternative approach, convolutional neural networks were trained for image-based, deep regression to predict numerical values corresponding to measurements and abstract properties such as age or health states directly. The numerical values resulting from this regression approach are not easily explainable, as no intermediate segmentation masks are generated. For an interpretation of the decision criteria learned by the networks, aggregated saliency analysis was proposed as a visualization technique for relevant anatomical structures in thousands of co-aligned subjects. Additionally, methods for uncertainty quantification were adapted to provide individual confidence intervals along with each prediction.By exploring different configurations and developing fast and effective strategies with these two methodologies, several software tools were implemented that can robustly predict measurements for thousands of UK Biobank subjects within hours, with no requirement for human guidance or intervention.
|
|
| 3. |
- Langner, Taro, et al.
(författare)
-
Fully convolutional networks for automated segmentation of abdominal adipose tissue depots in multicenter water–fat MRI
- 2019
-
Ingår i: Magnetic Resonance in Medicine. - : Wiley. - 0740-3194 .- 1522-2594. ; 81:4, s. 2736-2745
-
Tidskriftsartikel (refereegranskat)abstract
- Purpose: An approach for the automated segmentation of visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) in multicenter water–fat MRI scans of the abdomen was investigated, using 2 different neural network architectures.Methods: The 2 fully convolutional network architectures U‐Net and V‐Net were trained, evaluated, and compared using the water–fat MRI data. Data of the study Tellus with 90 scans from a single center was used for a 10‐fold cross‐validation in which the most successful configuration for both networks was determined. These configurations were then tested on 20 scans of the multicenter study beta‐cell function in JUvenile Diabetes and Obesity (BetaJudo), which involved a different study population and scanning device.Results: The U‐Net outperformed the used implementation of the V‐Net in both cross‐validation and testing. In cross‐validation, the U‐Net reached average dice scores of 0.988 (VAT) and 0.992 (SAT). The average of the absolute quantification errors amount to 0.67% (VAT) and 0.39% (SAT). On the multicenter test data, the U‐Net performs only slightly worse, with average dice scores of 0.970 (VAT) and 0.987 (SAT) and quantification errors of 2.80% (VAT) and 1.65% (SAT).Conclusion: The segmentations generated by the U‐Net allow for reliable quantification and could therefore be viable for high‐quality automated measurements of VAT and SAT in large‐scale studies with minimal need for human intervention. The high performance on the multicenter test data furthermore shows the robustness of this approach for data of different patient demographics and imaging centers, as long as a consistent imaging protocol is used.
|
|
| 4. |
- Langner, Taro, et al.
(författare)
-
Identifying morphological indicators of aging with neural networks on large-scale whole-body MRI
- 2020
-
Ingår i: IEEE Transactions on Medical Imaging. - 0278-0062 .- 1558-254X. ; 39:5, s. 1430-1437
-
Tidskriftsartikel (refereegranskat)abstract
- A wealth of information is contained in images obtained by whole-body magnetic resonance imaging (MRI). Studying the link between the imaged anatomy and properties known from outside sources has the potential to give new insights into the underlying factors that manifest themselves in individual human morphology. In this work we investigate the expression of age-related changes in the whole-body image. A large dataset of about 32,000 subjects scanned from neck to knee and aged 44–82 years from the UK Biobank study was used for a machine-based analysis. We trained a convolutional neural network based on the VGG16 architecture to predict the age of a given subject based on image data from these scans. In 10-fold cross-validation on 23,000 of these images the network reached a mean absolute error (MAE) of 2.49 years (R 2 = 0.83) and showed consistent performance on a separate test set of another 8,000 images. On a second test set of 100 images the network outperformed the averaged estimates given by three experienced radiologists, which reached an MAE of 5.58 years (R 2 = 0.08), by more than three years on average. In an attempt to explain these findings, we employ saliency analysis that opens up the image-based criteria used by the automated method to human interpretation. We aggregate the saliency into a single anatomical visualization which clearly highlights structures in the aortic arch and knee as primary indicators of age.
|
|
| 5. |
- Langner, Taro, et al.
(författare)
-
Kidney segmentation in neck-to-knee body MRI of 40,000 UK Biobank participants
- 2020
-
Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 10:1
-
Tidskriftsartikel (refereegranskat)abstract
- The UK Biobank is collecting extensive data on health-related characteristics of over half a million volunteers. The biological samples of blood and urine can provide valuable insight on kidney function, with important links to cardiovascular and metabolic health. Further information on kidney anatomy could be obtained by medical imaging. In contrast to the brain, heart, liver, and pancreas, no dedicated Magnetic Resonance Imaging (MRI) is planned for the kidneys. An image-based assessment is nonetheless feasible in the neck-to-knee body MRI intended for abdominal body composition analysis, which also covers the kidneys. In this work, a pipeline for automated segmentation of parenchymal kidney volume in UK Biobank neck-to-knee body MRI is proposed. The underlying neural network reaches a relative error of 3.8%, with Dice score 0.956 in validation on 64 subjects, close to the 2.6% and Dice score 0.962 for repeated segmentation by one human operator. The released MRI of about 40,000 subjects can be processed within one day, yielding volume measurements of left and right kidney. Algorithmic quality ratings enabled the exclusion of outliers and potential failure cases. The resulting measurements can be studied and shared for large-scale investigation of associations and longitudinal changes in parenchymal kidney volume.
|
|
| 6. |
- Langner, Taro, et al.
(författare)
-
Large-scale biometry with interpretable neural network regression on UK Biobank body MRI
- 2020
-
Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 10:1
-
Tidskriftsartikel (refereegranskat)abstract
- In a large-scale medical examination, the UK Biobank study has successfully imaged more than 32,000 volunteer participants with magnetic resonance imaging (MRI). Each scan is linked to extensive metadata, providing a comprehensive medical survey of imaged anatomy and related health states. Despite its potential for research, this vast amount of data presents a challenge to established methods of evaluation, which often rely on manual input. To date, the range of reference values for cardiovascular and metabolic risk factors is therefore incomplete. In this work, neural networks were trained for image-based regression to infer various biological metrics from the neck-to-knee body MRI automatically. The approach requires no manual intervention or direct access to reference segmentations for training. The examined fields span 64 variables derived from anthropometric measurements, dual-energy X-ray absorptiometry (DXA), atlas-based segmentations, and dedicated liver scans. With the ResNet50, the standardized framework achieves a close fit to the target values (median R2 > 0.97) in cross-validation. Interpretation of aggregated saliency maps suggests that the network correctly targets specific body regions and limbs, and learned to emulate different modalities. On several body composition metrics, the quality of the predictions is within the range of variability observed between established gold standard techniques.
|
|
| 7. |
|
|
| 8. |
- Langner, Taro, et al.
(författare)
-
Large-scale Inference of Liver Fat with Neural Networks on UK Biobank Body MRI
- 2020
-
Ingår i: International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2020. - Cham : Springer. - 9783030597122 - 9783030597139 ; , s. 602-611
-
Konferensbidrag (refereegranskat)abstract
- The UK Biobank Imaging Study has acquired medical scans of more than 40,000 volunteer participants. The resulting wealth of anatomical information has been made available for research, together with extensive metadata including measurements of liver fat. These values play an important role in metabolic disease, but are only available for a minority of imaged subjects as their collection requires the careful work of image analysts on dedicated liver MRI. Another UK Biobank protocol is neck-to-knee body MRI for analysis of body composition. The resulting volumes can also quantify fat fractions, even though they were reconstructed with a two- instead of a three-point Dixon technique. In this work, a novel framework for automated inference of liver fat from UK Biobank neck-to-knee body MRI is proposed. A ResNet50 was trained for regression on two-dimensional slices from these scans and the reference values as target, without any need for ground truth segmentations. Once trained, it performs fast, objective, and fully automated predictions that require no manual intervention. On the given data, it closely emulates the reference method, reaching a level of agreement comparable to different gold standard techniques. The network learned to rectify non-linearities in the fat fraction values and identified several outliers in the reference. It outperformed a multi-atlas segmentation baseline and inferred new estimates for all imaged subjects lacking reference values, expanding the total number of liver fat measurements by factor six.
|
|
| 9. |
- Langner, Taro, et al.
(författare)
-
MIMIR : Deep Regression for Automated Analysis of UK Biobank MRI Scans
- 2022
-
Ingår i: Radiology: Artificial Intelligence. - : Radiological Society of North America (RSNA). - 2638-6100. ; 4:3
-
Tidskriftsartikel (refereegranskat)abstract
- UK Biobank (UKB) has recruited more than 500000 volunteers from the United Kingdom, collecting health-related information on genetics, lifestyle, blood biochemistry, and more. Ongoing medical imaging of 100 000 participants with 70 000 follow-up sessions will yield up to 170 000 MRI scans, enabling image analysis of body composition, organs, and muscle. This study presents an experimental inference engine for automated analysis of UKB neck-to-knee body 1.5-T MRI scans. This retrospective cross-validation study includes data from 38 916 participants (52% female; mean age, 64 years) to capture baseline characteristics, such as age, height, weight, and sex, as well as measurements of body composition, organ volumes, and abstract properties, such as grip strength, pulse rate, and type 2 diabetes status. Prediction intervals for each end point were generated based on uncertainty quantification. On a subsequent release of UKB data, the proposed method predicted 12 body composition metrics with a 3% median error and yielded mostly well-calibrated individual prediction intervals. The processing of MRI scans from 1000 participants required 10 minutes. The underlying method used convolutional neural networks for image-based mean-variance regression on two-dimensional representations of the MRI data. An implementation was made publicly available for fast and fully automated estimation of 72 different measurements from future releases of UKB image data. (C) RSNA, 2022.
|
|
| 10. |
- Langner, Taro, et al.
(författare)
-
Uncertainty-Aware Body Composition Analysis with Deep Regression Ensembles on UK Biobank MRI
- 2021
-
Ingår i: Computerized Medical Imaging and Graphics. - : Elsevier BV. - 0895-6111 .- 1879-0771. ; 93
-
Tidskriftsartikel (refereegranskat)abstract
- Along with rich health-related metadata, an ongoing imaging study has acquired MRI of over 40,000 male and female UK Biobank participants aged 44-82 since 2014. Phenotypes derived from these images, such as measurements of body composition, can reveal new links between genetics, cardiovascular disease, and metabolic conditions. In this retrospective study, six measurements of body composition were automatically estimated by ResNet50 neural networks for image-based regression from neck-to-knee body MRI. Despite the potential for high speed and accuracy, these networks produce no output segmentations that could indicate the reliability of individual measurements. The presented experiments therefore examine mean-variance regression and ensembling for predictive uncertainty estimation, which can quantify individual measurement errors and thereby help to identify potential outliers, anomalies, and other failure cases automatically. In 10-fold cross-validation on data of about 8,500 subjects, mean-variance regression and ensembling showed complementary benefits, reducing the mean absolute error across all predictions by 12%. Both improved the calibration of uncertainties and their ability to identify high prediction errors. With intra-class correlation coefficients (ICC) above 0.97, all targets except the liver fat content yielded relative measurement errors below 5%. Testing on another 1,000 subjects showed consistent performance, and the method was finally deployed for inference to 30,000 subjects with missing reference values. The results indicate that deep regression ensembles could ultimately provide automated, uncertainty-aware measurements of body composition for more than 120,000 UK Biobank neck-to-knee body MRI that are to be acquired within the coming years.
|
|
