| 1. |
- Augustin, Xenia, et al.
(författare)
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Estimating Mean Speed-of-Sound from Sequence-Dependent Geometric Disparities
- 2021
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Ingår i: INTERNATIONAL ULTRASONICS SYMPOSIUM (IEEE IUS 2021). - : Institute of Electrical and Electronics Engineers (IEEE). - 9780738112091
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Konferensbidrag (refereegranskat)abstract
- In ultrasound beamforming, focusing time delays are typically computed with a spatially constant speed-of-sound (SoS) assumption. A mismatch between beamforming and true medium SoS then leads to aberration artifacts. Other imaging techniques such as spatially-resolved SoS reconstruction using tomographic techniques also rely on a good SoS estimate for initial beamforming. In this work, we exploit spatially-varying geometric disparities in the transmit and receive paths of multiple sequences for estimating a mean medium SoS. We use images from diverging waves beamformed with an assumed SoS, and propose a model fitting method for estimating the SoS offset. We demonstrate the effectiveness of our proposed method for tomographic SoS reconstruction. With corrected beamforming SoS, the reconstruction accuracy on simulated data was improved by 63% and 29%, respectively, for an initial SoS over- and under-estimation of 1.5%. We further demonstrate our proposed method on a breast phantom, indicating substantial improvement in contrast-to-noise ratio for local SoS mapping.
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| 2. |
- Chintada, Bhaskara R, et al.
(författare)
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Time Of Arrival Delineation In Echo Traces For Reflection Ultrasound Tomography
- 2021
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Konferensbidrag (refereegranskat)abstract
- Ultrasound Computed Tomography (USCT) is an imaging method to map acoustic properties in soft tissues, e.g., for the diagnosis of breast cancer. A group of USCT methods rely on a passive reflector behind the imaged tissue, and they function by delineating such reflector in echo traces, e.g., to infer time-of-flight measurements for reconstructing local speed-of-sound maps. In this work, we study various echo features and delineation methods to robustly identify reflector profiles in echos. We compared and evaluated the methods on a multi-static data set of a realistic breast phantom. Based on our results, a RANSAC based outlier removal followed by an active contours based delineation using a new “edge” feature we propose that detects the first arrival times of echo performs robustly even in complex media; in particular 2.1 times superior to alternative approaches at locations where diffraction effects are prominent.
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| 3. |
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| 4. |
- Gomariz, Alvaro, et al.
(författare)
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Modality attention and sampling enables deep learning with heterogeneous marker combinations in fluorescence microscopy
- 2021
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Ingår i: Nature Machine Intelligence. - : Springer Nature. - 2522-5839. ; 3:9, s. 799-811
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Tidskriftsartikel (refereegranskat)abstract
- Fluorescence microscopy allows for a detailed inspection of cells, cellular networks and anatomical landmarks by staining with a variety of carefully selected markers visualized as colour channels. Quantitative characterization of structures in acquired images often relies on automatic image analysis methods. Despite the success of deep learning methods in other vision applications, their potential for fluorescence image analysis remains underexploited. One reason lies in the considerable workload required to train accurate models, which are normally specific for a given combination of markers and therefore applicable to a very restricted number of experimental settings. We herein propose ‘marker sampling and excite’—a neural network approach with a modality sampling strategy and a novel attention module that together enable (1) flexible training with heterogeneous datasets with combinations of markers and (2) successful utility of learned models on arbitrary subsets of markers prospectively. We show that our single neural network solution performs comparably to an upper bound scenario in which an ensemble of many networks is naively trained for each possible marker combination separately. We also demonstrate the feasibility of this framework in high-throughput biological analysis by revising a recent quantitative characterization of bone-marrow vasculature in three-dimensional confocal microscopy datasets and further confirm the validity of our approach on another substantially different dataset of microvessels in foetal liver tissues. Not only can our work substantially ameliorate the use of deep learning in fluorescence microscopy analysis, but it can also be utilized in other fields with incomplete data acquisitions and missing modalities.
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| 5. |
- Jaume, Guillaume, et al.
(författare)
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Quantifying Explainers of Graph Neural Networks in Computational Pathology
- 2021
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Ingår i: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665445092 - 9781665445108 ; , s. 8102-8112
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Konferensbidrag (refereegranskat)abstract
- Explainability of deep learning methods is imperative to facilitate their clinical adoption in digital pathology. However, popular deep learning methods and explainability techniques (explainers) based on pixel-wise processing disregard biological entities' notion, thus complicating comprehension by pathologists. In this work, we address this by adopting biological entity-based graph processing and graph explainers enabling explanations accessible to pathologists. In this context, a major challenge becomes to discern meaningful explainers, particularly in a standardized and quantifiable fashion. To this end, we propose herein a set of novel quantitative metrics based on statistics of class separability using pathologically measurable concepts to characterize graph explainers. We employ the proposed metrics to evaluate three types of graph explainers, namely the layer-wise relevance propagation, gradient-based saliency, and graph pruning approaches, to explain Cell-Graph representations for Breast Cancer Subtyping. The proposed metrics are also applicable in other domains by using domain-specific intuitive concepts. We validate the qualitative and quantitative findings on the BRACS dataset, a large cohort of breast cancer RoIs, by expert pathologists.
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| 6. |
- Zhang, Lin, et al.
(författare)
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Learning ultrasound rendering from cross-sectional model slices for simulated training
- 2021
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Ingår i: International Journal of Computer Assisted Radiology and Surgery. - : Springer. - 1861-6410 .- 1861-6429. ; 16:5, s. 721-730
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Tidskriftsartikel (refereegranskat)abstract
- PurposeGiven the high level of expertise required for navigation and interpretation of ultrasound images, computational simulations can facilitate the training of such skills in virtual reality. With ray-tracing based simulations, realistic ultrasound images can be generated. However, due to computational constraints for interactivity, image quality typically needs to be compromised.MethodsWe propose herein to bypass any rendering and simulation process at interactive time, by conducting such simulations during a non-time-critical offline stage and then learning image translation from cross-sectional model slices to such simulated frames. We use a generative adversarial framework with a dedicated generator architecture and input feeding scheme, which both substantially improve image quality without increase in network parameters. Integral attenuation maps derived from cross-sectional model slices, texture-friendly strided convolutions, providing stochastic noise and input maps to intermediate layers in order to preserve locality are all shown herein to greatly facilitate such translation task.ResultsGiven several quality metrics, the proposed method with only tissue maps as input is shown to provide comparable or superior results to a state-of-the-art that uses additional images of low-quality ultrasound renderings. An extensive ablation study shows the need and benefits from the individual contributions utilized in this work, based on qualitative examples and quantitative ultrasound similarity metrics. To that end, a local histogram statistics based error metric is proposed and demonstrated for visualization of local dissimilarities between ultrasound images.ConclusionA deep-learning based direct transformation from interactive tissue slices to likeness of high quality renderings allow to obviate any complex rendering process in real-time, which could enable extremely realistic ultrasound simulations on consumer-hardware by moving the time-intensive processes to a one-time, offline, preprocessing data preparation stage that can be performed on dedicated high-end hardware.
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