| 1. |
- Bezek, Can Deniz, et al.
(författare)
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Global Speed-of-Sound Prediction Using Transmission Geometry
- 2022
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Ingår i: Proceedings of the 2022 IEEE International Ultrasonics Symposium (IUS). - : IEEE. - 9781665466578 - 9781665478137 ; , s. 1-4
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Konferensbidrag (refereegranskat)abstract
- Most ultrasound (US) imaging techniques usespatially-constant speed-of-sound (SoS) values for beamforming.Having a discrepancy between the actual and used SoS valueleads to aberration artifacts, e.g., reducing the image resolution,which may affect diagnostic usability. Accuracy and quality ofdifferent US imaging modalities, such as tomographic reconstruc-tion of local SoS maps, also depend on a good initial beamformingSoS. In this work, we develop an analytical method for estimatingmean SoS in an imaged medium. We show that the relative shiftsbetween beamformed frames depend on the SoS offset and thegeometric disparities in transmission paths. Using this relation,we estimate a correction factor and hence a corrected mean SoSin the medium. We evaluated our proposed method on a set ofnumerical simulations, demonstrating its utility both for globalSoS prediction and for local SoS tomographic reconstruction.For our evaluation dataset, for an initial SoS under- and over-assumption of 5% the medium SoS, our method is able to predictthe actual mean SoS within 0.3% accuracy. For the tomographicreconstruction of local SoS maps, the reconstruction accuracy isimproved on average by 78.5% and 87%, respectively, comparedto an initial SoS under- and over-assumption of 5%.Index Terms—Beamforming, aberration correction.
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| 2. |
- Chintada, Bhaskara Rao, et al.
(författare)
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Spectral Ultrasound Imaging of Speed-of-Sound and Attenuation Using an Acoustic Mirror
- 2022
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Ingår i: Frontiers in Physics. - : Frontiers Media S.A.. - 2296-424X. ; 10
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Tidskriftsartikel (refereegranskat)abstract
- Speed-of-sound and attenuation of ultrasound waves vary in the tissues. There exist methods in the literature that allow for spatially reconstructing the distribution of group speed-of-sound (SoS) and frequency-dependent ultrasound attenuation (UA) using reflections from an acoustic mirror positioned at a known distance from the transducer. These methods utilize a conventional ultrasound transducer operating in pulse-echo mode and a calibration protocol with measurements in water. In this study, we introduce a novel method for reconstructing local SoS and UA maps as a function of acoustic frequency through Fourier-domain analysis and by fitting linear and power-law dependency models in closed form. Frequency-dependent SoS and UA together characterize the tissue comprehensively in spectral domain within the utilized transducer bandwidth. In simulations, our proposed methods are shown to yield low reconstruction error: 0.01 dB/cm.MHz(y) for attenuation coefficient and 0.05 for the frequency exponent. For tissue-mimicking phantoms and ex-vivo bovine muscle samples, a high reconstruction contrast was achieved. Attenuation exponents in a gelatin-cellulose mixture and an ex-vivo bovine muscle sample were found to be, respectively, 1.3 and 0.6 on average. Linear dispersion of SoS in a gelatin-cellulose mixture and an ex-vivo bovine muscle sample were found to be, respectively, 1.3 and 4.0 m/s.MHz on average. These findings were reproducible when the inclusion and substrate materials were exchanged. Bulk loss modulus in the bovine muscle sample was computed to be approximately 4 times the bulk loss modulus in the gelatin-cellulose mixture. Such frequency-dependent characteristics of SoS and UA, and bulk loss modulus may therefore differentiate tissues as potential diagnostic biomarkers.
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| 3. |
- Gomariz, Alvaro, et al.
(författare)
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Probabilistic spatial analysis in quantitative microscopy with uncertainty-aware cell detection using deep Bayesian regression
- 2022
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Ingår i: Science Advances. - : American Association for the Advancement of Science (AAAS). - 2375-2548. ; 8:5
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Tidskriftsartikel (refereegranskat)abstract
- The investigation of biological systems with three-dimensional microscopy demands automatic cell identification methods that not only are accurate but also can imply the uncertainty in their predictions. The use of deep learning to regress density maps is a popular successful approach for extracting cell coordinates from local peaks in a postprocessing step, which then, however, hinders any meaningful probabilistic output. We propose a framework that can operate on large microscopy images and output probabilistic predictions (i) by integrating deep Bayesian learning for the regression of uncertainty-aware density maps, where peak detection algorithms generate cell proposals, and (ii) by learning a mapping from prediction proposals to a probabilistic space that accurately represents the chances of a successful prediction. Using these calibrated predictions, we propose a probabilistic spatial analysis with Monte Carlo sampling. We demonstrate this in a bone marrow dataset, where our proposed methods reveal spatial patterns that are otherwise undetectable.
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| 4. |
- Gomariz, Alvaro, et al.
(författare)
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Unsupervised Domain Adaptation with Contrastive Learning for OCT Segmentation
- 2022
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Ingår i: Medical Image Computing and Computer Assisted Intervention, MICCAI 2022, pt viii. - Cham : Springer Nature. - 9783031164521 - 9783031164514 ; , s. 351-361
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Konferensbidrag (refereegranskat)abstract
- Accurate segmentation of retinal fluids in 3D Optical Coherence Tomography images is key for diagnosis and personalized treatment of eye diseases. While deep learning has been successful at this task, trained supervised models often fail for images that do not resemble labeled examples, e.g. for images acquired using different devices. We hereby propose a novel semi-supervised learning framework for segmentation of volumetric images from new unlabeled domains. We jointly use supervised and contrastive learning, also introducing a contrastive pairing scheme that leverages similarity between nearby slices in 3D. In addition, we propose channel-wise aggregation as an alternative to conventional spatial-pooling aggregation for contrastive feature map projection. We evaluate our methods for domain adaptation from a (labeled) source domain to an (unlabeled) target domain, each containing images acquired with different acquisition devices. In the target domain, our method achieves a Dice coefficient 13.8% higher than SimCLR (a state-of-the-art contrastive framework), and leads to results comparable to an upper bound with supervised training in that domain. In the source domain, our model also improves the results by 5.4% Dice, by successfully leveraging information from many unlabeled images.
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| 5. |
- Pati, Pushpak, et al.
(författare)
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Hierarchical graph representations in digital pathology
- 2022
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Ingår i: Medical Image Analysis. - : Elsevier. - 1361-8415 .- 1361-8423. ; 75
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Tidskriftsartikel (refereegranskat)abstract
- Cancer diagnosis, prognosis, and therapy response predictions from tissue specimens highly depend on the phenotype and topological distribution of constituting histological entities. Thus, adequate tissue representations for encoding histological entities is imperative for computer aided cancer patient care. To this end, several approaches have leveraged cell-graphs, capturing the cell-microenvironment, to depict the tissue. These allow for utilizing graph theory and machine learning to map the tissue representation to tissue functionality, and quantify their relationship. Though cellular information is crucial, it is incomplete alone to comprehensively characterize complex tissue structure. We herein treat the tissue as a hierarchical composition of multiple types of histological entities from fine to coarse level, capturing multivariate tissue information at multiple levels. We propose a novel multi-level hierarchical entity graph representation of tissue specimens to model the hierarchical compositions that encode histological entities as well as their intra-and inter-entity level interactions. Subsequently, a hierarchical graph neural network is proposed to operate on the hierarchical entity-graph and map the tissue structure to tissue functionality. Specifically, for input histology images, we utilize well-defined cells and tissue regions to build HierArchical Cell-to-Tissue ( HACT ) graph representations, and devise HACT-Net, a message passing graph neural network, to classify the HACT representations. As part of this work, we introduce the BReAst Carcinoma Subtyping (BRACS) dataset, a large cohort of Haematoxylin & Eosin stained breast tumor regions-of-interest, to evaluate and benchmark our proposed methodology against pathologists and state-of-the-art computer-aided diagnostic approaches. Through comparative assessment and ablation studies, our proposed method is demonstrated to yield superior classification results compared to alternative methods as well as individual pathologists. The code, data, and models can be accessed at https://github.com/histocartography/hact-net . (c) 2021 Elsevier B.V. All rights reserved.
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| 6. |
- Pean, Fabien, et al.
(författare)
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Computational analysis of subscapularis tears and pectoralis major transfers on muscular activity
- 2022
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Ingår i: Clinical Biomechanics. - : Elsevier. - 0268-0033 .- 1879-1271. ; 92
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Tidskriftsartikel (refereegranskat)abstract
- Background: Pectoralis major is the most common muscle transfer procedure to restore joint function after subscapularis tears. Limited information is available on how the neuromuscular system adjusts to the new configuration, which could explain the mixed outcomes of the procedure. The purpose of this study is to assess how muscles activation patterns change after pectoralis major transfers and report their biomechanical implications.Methods: We compare how muscle activation change with subscapularis tears and after its treatment by pectoralis major transfers of the clavicular, sternal, or both these segments, during three activities of daily living and a computational musculoskeletal model of the shoulder.Findings: Our results indicate that subscapularis tears require a compensatory activation of the supraspinatus and is accompanied by a reduced co-contraction of the infraspinatus, both of which can be partially recovered after transfer. Furthermore, although the pectoralis major acts asynchronously to the subscapularis before the transfer, its activation pattern changes significantly after the transfer.Interpretation: The capability of a transferred muscle segment to activate similarly to the intact subscapularis is found to be dependent on the given motion. Differences in the activation patterns between intact subscapularis and the segments of pectoralis major may explain the difficulty in adapting psycho-motor patterns during the rehabilitation period. There by, rehabilitation programs could benefit from targeted training on specific motion and biofeedback programs. Finally, the condition of the anterior deltoid should be considered to improve joint function.
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| 7. |
- Thandiackal, Kevin, et al.
(författare)
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Differentiable Zooming for Multiple Instance Learning on Whole-Slide Images
- 2022
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Ingår i: COMPUTER VISION, ECCV 2022, PT XXI. - Cham : Springer Nature. - 9783031198021 - 9783031198038 ; , s. 699-715
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Konferensbidrag (refereegranskat)abstract
- Multiple Instance Learning (MIL) methods have become increasingly popular for classifying gigapixel-sized Whole-Slide Images (WSIs) in digital pathology. Most MIL methods operate at a single WSI magnification, by processing all the tissue patches. Such a formulation induces high computational requirements and constrains the contextualization of the WSI-level representation to a single scale. Certain MIL methods extend to multiple scales, but they are computationally more demanding. In this paper, inspired by the pathological diagnostic process, we propose ZoomMIL, a method that learns to perform multi-level zooming in an end-to-end manner. ZoomMIL builds WSI representations by aggregating tissue-context information from multiple magnifications. The proposed method outperforms the state-of-the-art MIL methods in WSI classification on two large datasets, while significantly reducing computational demands with regard to Floating-Point Operations (FLOPs) and processing time by 40-50x. Our code is available at: https://github. com/histocartography/zoommil.
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