| 1. |
- Abbaspour, S., et al.
(författare)
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Real-Time and Offline Evaluation of Myoelectric Pattern Recognition for the Decoding of Hand Movements
- 2021
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Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 21:16
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Tidskriftsartikel (refereegranskat)abstract
- Pattern recognition algorithms have been widely used to map surface electromyographic signals to target movements as a source for prosthetic control. However, most investigations have been conducted offline by performing the analysis on pre-recorded datasets. While real-time data analysis (i.e., classification when new data becomes available, with limits on latency under 200-300 milliseconds) plays an important role in the control of prosthetics, less knowledge has been gained with respect to real-time performance. Recent literature has underscored the differences between offline classification accuracy, the most common performance metric, and the usability of upper limb prostheses. Therefore, a comparative offline and real-time performance analysis between common algorithms had yet to be performed. In this study, we investigated the offline and real-time performance of nine different classification algorithms, decoding ten individual hand and wrist movements. Surface myoelectric signals were recorded from fifteen able-bodied subjects while performing the ten movements. The offline decoding demonstrated that linear discriminant analysis (LDA) and maximum likelihood estimation (MLE) significantly (p < 0.05) outperformed other classifiers, with an average classification accuracy of above 97%. On the other hand, the real-time investigation revealed that, in addition to the LDA and MLE, multilayer perceptron also outperformed the other algorithms and achieved a classification accuracy and completion rate of above 68% and 69%, respectively.
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| 2. |
- Borrelli, P., et al.
(författare)
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Artificial intelligence-aided CT segmentation for body composition analysis: a validation study
- 2021
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Ingår i: European Radiology Experimental. - : Springer Science and Business Media LLC. - 2509-9280. ; 5:1
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundBody composition is associated with survival outcome in oncological patients, but it is not routinely calculated. Manual segmentation of subcutaneous adipose tissue (SAT) and muscle is time-consuming and therefore limited to a single CT slice. Our goal was to develop an artificial-intelligence (AI)-based method for automated quantification of three-dimensional SAT and muscle volumes from CT images.MethodsEthical approvals from Gothenburg and Lund Universities were obtained. Convolutional neural networks were trained to segment SAT and muscle using manual segmentations on CT images from a training group of 50 patients. The method was applied to a separate test group of 74 cancer patients, who had two CT studies each with a median interval between the studies of 3days. Manual segmentations in a single CT slice were used for comparison. The accuracy was measured as overlap between the automated and manual segmentations.ResultsThe accuracy of the AI method was 0.96 for SAT and 0.94 for muscle. The average differences in volumes were significantly lower than the corresponding differences in areas in a single CT slice: 1.8% versus 5.0% (p <0.001) for SAT and 1.9% versus 3.9% (p < 0.001) for muscle. The 95% confidence intervals for predicted volumes in an individual subject from the corresponding single CT slice areas were in the order of 20%.Conclusions The AI-based tool for quantification of SAT and muscle volumes showed high accuracy and reproducibility and provided a body composition analysis that is more relevant than manual analysis of a single CT slice.
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| 3. |
- Tampu, Iulian Emil, et al.
(författare)
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Deep-learning for thyroid microstructure segmentation in 2D OCT images
- 2021
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Ingår i: Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXV. - : SPIE - International Society for Optical Engineering.
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Konferensbidrag (refereegranskat)abstract
- Optical coherence tomography (OCT) can provide exquisite details of tissue microstructure without traditional tissue sectioning, with potential diagnostic and intraoperative applications in a variety of clinical areas. In thyroid surgery, OCT could provide information to reduce the risk of damaging normal tissue. Thyroid tissue's follicular structure alters in case of various pathologies including the non-malignant ones which can be imaged using OCT. The success of deep learning for medical image analysis encourages its application on OCT thyroid images for quantitative analysis of tissue microstructure. To investigate the potential of a deep learning approach to segment the follicular structure in OCT images, a 2D U-Net was trained on b-scan OCT images acquired from ex vivo adult human thyroid samples a effected by a range of pathologies. Results on a pool of 104 annotated images showed a mean Dice score of 0.74±0.19 and 0.92±0.09 when segmenting the follicular structure and the surrounding tissue on the test dataset (n=10 images). This study shows that a deep learning approach for tissue microstructure segmentation in OCT images is possible. The achieved performance without requiring manual intervention encourages the application of a deep-learning method for real-time analysis of OCT data.
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| 4. |
- Hedlund, Joel, 1978-, et al.
(författare)
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Key insights in the AIDA community policy on sharing of clinical imaging data for research in Sweden
- 2020
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Ingår i: Scientific Data. - : Springer Nature. - 2052-4463. ; 7
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Tidskriftsartikel (refereegranskat)abstract
- Development of world-class artificial intelligence (AI) for medical imaging requires access to massive amounts of training data from clinical sources, but effective data sharing is often hindered by uncertainty regarding data protection. We describe an initiative to reduce this uncertainty through a policy describing a national community consensus on sound data sharing practices.
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| 5. |
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| 6. |
- Chen, Heping, et al.
(författare)
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Real-Time Cerebral Vessel Segmentation in Laser Speckle Contrast Image Based on Unsupervised Domain Adaptation
- 2021
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Ingår i: Frontiers in Neuroscience. - : Frontiers Media SA. - 1662-4548 .- 1662-453X. ; 15
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Tidskriftsartikel (refereegranskat)abstract
- Laser speckle contrast imaging (LSCI) is a full-field, high spatiotemporal resolution and low-cost optical technique for measuring blood flow, which has been successfully used for neurovascular imaging. However, due to the low signal-noise ratio and the relatively small sizes, segmenting the cerebral vessels in LSCI has always been a technical challenge. Recently, deep learning has shown its advantages in vascular segmentation. Nonetheless, ground truth by manual labeling is usually required for training the network, which makes it difficult to implement in practice. In this manuscript, we proposed a deep learning-based method for real-time cerebral vessel segmentation of LSCI without ground truth labels, which could be further integrated into intraoperative blood vessel imaging system. Synthetic LSCI images were obtained with a synthesis network from LSCI images and public labeled dataset of Digital Retinal Images for Vessel Extraction, which were then used to train the segmentation network. Using matching strategies to reduce the size discrepancy between retinal images and laser speckle contrast images, we could further significantly improve image synthesis and segmentation performance. In the testing LSCI images of rodent cerebral vessels, the proposed method resulted in a dice similarity coefficient of over 75%.
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| 7. |
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| 8. |
- Lagerstrand, Kerstin M, et al.
(författare)
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Reliable phase-contrast flow volume magnetic resonance measurements are feasible without adjustment of the velocity encoding parameter
- 2020
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Ingår i: Journal of Medical Imaging. - 2329-4310 .- 2329-4302. ; 7:6
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: To show that adjustment of velocity encoding (VENC) for phase-contrast (PC) flow volume measurements is not necessary in modern MR scanners with effective background velocity offset corrections. Approach: The independence on VENC was demonstrated theoretically, but also experimentally on dedicated phantoms and on patients with chronic aortic regurgitation (n = 17) and one healthy volunteer. All PC measurements were performed using a modern MR scanner, where the pre-emphasis circuit but also a subsequent post-processing filter were used for effective correction of background velocity offset errors. Results: The VENC level strongly affected the velocity noise level in the PC images and, hence, the estimated peak flow velocity. However, neither the regurgitant blood flow volume nor the mean flow velocity displayed any clinically relevant dependency on the VENC level. Also, the background velocity offset was shown to be close to zero (
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| 9. |
- Mürer, Fredrik K., et al.
(författare)
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Quantifying the hydroxyapatite orientation near the ossification front in a piglet femoral condyle using X-ray diffraction tensor tomography
- 2021
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322 .- 2045-2322. ; 11:1
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Tidskriftsartikel (refereegranskat)abstract
- While a detailed knowledge of the hierarchical structure and morphology of the extracellular matrix is considered crucial for understanding the physiological and mechanical properties of bone and cartilage, the orientation of collagen fibres and carbonated hydroxyapatite (HA) crystallites remains a debated topic. Conventional microscopy techniques for orientational imaging require destructive sample sectioning, which both precludes further studies of the intact sample and potentially changes the microstructure. In this work, we use X-ray diffraction tensor tomography to image non-destructively in 3D the HA orientation in a medial femoral condyle of a piglet. By exploiting the anisotropic HA diffraction signal, 3D maps showing systematic local variations of the HA crystallite orientation in the growing subchondral bone and in the adjacent mineralized growth cartilage are obtained. Orientation maps of HA crystallites over a large field of view (~ 3 × 3 × 3 mm3) close to the ossification (bone-growth) front are compared with high-resolution X-ray propagation phase-contrast computed tomography images. The HA crystallites are found to predominantly orient with their crystallite c-axis directed towards the ossification front. Distinct patterns of HA preferred orientation are found in the vicinity of cartilage canals protruding from the subchondral bone. The demonstrated ability of retrieving 3D orientation maps of bone-cartilage structures is expected to give a better understanding of the physiological properties of bones, including their propensity for bone-cartilage diseases.
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| 10. |
- Phillips, Rachel, et al.
(författare)
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Visual Blood Loss Estimation Accuracy : Directions for Future Research Based on a Systematic Literature Review
- 2020
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Ingår i: Proceedings of the 2020 HFES 64th International Annual Meeting. - : Sage Publications. ; , s. 1411-1415
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Konferensbidrag (refereegranskat)abstract
- Visual blood loss estimation occurs in a variety of medical contexts and may impact everything from interventions by immediate responders to the likelihood of receiving blood transfusions in a hospital setting. However, research suggests that visual blood loss estimation is inaccurate for laypeople and medical professionals. The aim of the current study was to conduct a systematic literature review to determine the current state of knowledge on visual blood loss estimation accuracy and identify directions for future research. A structured search resulted in 1799 titles that were subsequently screened. A total of 72 articles were coded for comparison. Based on the evaluation, several gaps were identified, most notably related to factors of the situation that may influence estimation accuracy such as blood flow and victim/patient gender. Directions for future research are proposed based on identified gaps.
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