Deep Learning in Ultrasound Imaging

Sloun, Ruud J. G. van; Cohen, Regev; Eldar, Yonina C.

doi:10.1109/jproc.2019.2932116

Cited by 225 publications

(132 citation statements)

References 116 publications

(124 reference statements)

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“…Several attempts to use machine learning and deep learning for US-based disease diagnosis and characterisation have been reported [8]. For instance, super-resolution in US localisation microscopy and beamforming uses deep learning techniques for the removal of artefacts in element-wise complex in-phase and quadrature data [9].…”

Section: Introductionmentioning

confidence: 99%

A Learning-Based Microultrasound System for the Detection of Inflammation of the Gastrointestinal Tract

Yang

Lemke

Cox

et al. 2021

IEEE Trans. Med. Imaging

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Inflammation of the gastrointestinal (GI) tract accompanies several diseases, including Crohn's disease. Currently, video capsule endoscopy and deep bowel enteroscopy are the main means for direct visualisation of the bowel surface. However, the use of optical imaging limits visualisation to the luminal surface only, which makes early-stage diagnosis difficult. In this study, we propose a learning enabled microultrasound (µUS) system that aims to classify inflamed and non-inflamed bowel tissues. µUS images of the caecum, small bowel and colon were obtained from mice treated with agents to induce inflammation. Those images were then used to train three deep learning networks and to provide a ground truth of inflammation status. The classification accuracy was evaluated using 10-fold evaluation and additional B-scan images. Our deep learning approach allowed robust differentiation between healthy tissue and tissue with early signs of inflammation that is not detectable by current endoscopic methods or by human inspection of the µUS images. The methods may be a foundation for future early GI disease diagnosis and enhanced management with computer-aided imaging.

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Section: Introductionmentioning

confidence: 99%

A Learning-Based Microultrasound System for the Detection of Inflammation of the Gastrointestinal Tract

Yang

Lemke

Cox

et al. 2021

IEEE Trans. Med. Imaging

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“…In their seminal work, Gregor and LeCun unfolded the Iterative Shrinkage-Thresholding Algorithm (ISTA) for sparse coding [20,21] into a learned ISTA (LISTA) network, and demonstrated that propagating the data through only 10 blocks is equivalent to running the iterative algorithm for 200 iterations, without requiring fine-tuning of any parameters. In recent years, the concept of algorithm unfolding has been applied to many different problems, including, among others, single-image-superresolution (deblurring) [22,23], image denoising and image inpainting [24], ultrasound localization microscopy [25], ultrasound clutter suppression [26], and multi-channel source separation [27].…”

Section: Introductionmentioning

confidence: 99%

Learned SPARCOM: Unfolded Deep Super-Resolution Microscopy

Dardikman-Yoffe

Eldar

2020

Preprint

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The use of photo-activated fluorescent molecules to create long sequences of low emitter-density diffraction-limited images enables high-precision emitter localization. However, this is achieved at the cost of lengthy imaging times, limiting temporal resolution. In recent years, a variety of approaches have been suggested to reduce imaging times, ranging from classical optimization and statistical algorithms to deep learning methods. Classical methods often rely on prior knowledge of the optical system and require heuristic adjustment of parameters or do not lead to good enough performance. Deep learning methods proposed to date tend to suffer from poor generalization ability outside the specific distribution they were trained on, and require learning of many parameters. They also tend to lead to black-box solutions that are hard to interpret. In this paper, we suggest combining a recent high-performing classical method, SPARCOM, with model-based deep learning, using the algorithm unfolding approach which relies on an iterative algorithm to design a compact neural network considering domain knowledge. We show that the resulting network, Learned SPARCOM (LSPARCOM), requires far fewer layers and parameters, and can be trained on a single field of view. Nonetheless it yields comparable or superior results to those obtained by SPARCOM with no heuristic parameter determination or explicit knowledge of the point spread function, and is able to generalize better than standard deep learning techniques. It even allows producing a high-quality reconstruction from as few as 25 frames. This is due to a significantly smaller network, which also contributes to fast performance -5× improvement in execution time relative to SPARCOM, and a full order of magnitudes improvement relative to a leading competing deep learning method (Deep-STORM) when implemented serially. Our results show that we can obtain super-resolution imaging from a small number of high emitter density frames without knowledge of the optical system and across different test sets. Thus, we believe LSPARCOM will find broad use in single molecule localization microscopy of biological structures, and pave the way to interpretable, efficient live-cell imaging in a broad range of settings.

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“…Recently, automatic image analysis by machine and deep learning (DL) methods have already shown promise for reconstruction, classification, regression and segmentation of tissues using ultrasound images [16], [17]. In this paper we describe the use of DL to assist clinicians in detecting COVID-19 associated imaging patterns on point-of-care LUS.…”

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confidence: 99%

Deep Learning for Classification and Localization of COVID-19 Markers in Point-of-Care Lung Ultrasound

Roy

Menapace

Oei

et al. 2020

IEEE Trans. Med. Imaging

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397

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Deep learning (DL) has proved successful in medical imaging and, in the wake of the recent COVID-19 pandemic, some works have started to investigate DLbased solutions for the assisted diagnosis of lung diseases. While existing works focus on CT scans, this paper studies the application of DL techniques for the analysis of lung ultrasonography (LUS) images. Specifically, we present a novel fully-annotated dataset of LUS images collected from several Italian hospitals, with labels indicating the degree of disease severity at a frame-level, videolevel, and pixel-level (segmentation masks). Leveraging these data, we introduce several deep models that address relevant tasks for the automatic analysis of LUS images. In particular, we present a novel deep network, derived from Spatial Transformer Networks, which simultaneously predicts the disease severity score associated to a input frame and provides localization of pathological artefacts in a weakly-supervised way. Furthermore, we introduce a new method based on uninorms for effective frame score aggregation at a video-level. Finally, we benchmark state of the art deep models for estimating pixel-level segmentations of COVID-19 imaging biomarkers. Experiments on the proposed dataset demonstrate satisfactory results on all the considered tasks, paving the way to future research on DL for the assisted diagnosis of COVID-19 from LUS data.

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Deep Learning in Ultrasound Imaging

Cited by 225 publications

References 116 publications

A Learning-Based Microultrasound System for the Detection of Inflammation of the Gastrointestinal Tract

A Learning-Based Microultrasound System for the Detection of Inflammation of the Gastrointestinal Tract

Learned SPARCOM: Unfolded Deep Super-Resolution Microscopy

Deep Learning for Classification and Localization of COVID-19 Markers in Point-of-Care Lung Ultrasound

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