Mini-COVIDNet: Efficient Lightweight Deep Neural Network for Ultrasound Based Point-of-Care Detection of COVID-19

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LVNet: Light-Weight Model for Left Ventricle Segmentation for Short Axis Views in Echocardiographic Imaging

Lightweight segmentation models are becoming more popular for fast diagnosis on small and low cost medical imaging devices. This study focuses on the segmentation of the left ventricle (LV) in cardiac ultrasound (US) images. A new lightweight model [LV network (LVNet)] is proposed for segmentation, which gives the benefits of requiring fewer parameters but with improved segmentation performance in terms of Dice score (DS). The proposed model is compared with state-of-the-art methods, such as UNet, MiniNetV2, and fully convolutional dense dilated network (FCdDN). The model proposed comes with a post-processing pipeline that further enhances the segmentation results. In general, the training is done directly using the segmentation mask as the output and the US image as the input of the model. A new strategy for segmentation is also introduced in addition to the direct training method used. Compared with the UNet model, an improvement in DS performance as high as 5% for segmentation with papillary (WP) muscles was found, while showcasing an improvement of 18.5% when the papillary muscles are excluded. The model proposed requires only 5% of the memory required by a UNet model. LVNet achieves a better trade-off between the number of parameters and its segmentation performance as compared with other conventional models.</p

Multi-Threshold Attention U-Net (MTAU) based Model for Multimodal Brain Tumor Segmentation in MRI scans

Gliomas are one of the most frequent brain tumors and are classified into high grade and low grade gliomas. The segmentation of various regions such as tumor core, enhancing tumor etc. plays an important role in determining severity and prognosis. Here, we have developed a multi-threshold model based on attention U-Net for identification of various regions of the tumor in magnetic resonance imaging (MRI). We propose a multi-path segmentation and built three separate models for the different regions of interest. The proposed model achieved mean Dice Coefficient of 0.59, 0.72, and 0.61 for enhancing tumor, whole tumor and tumor core respectively on the training dataset. The same model gave mean Dice Coefficient of 0.57, 0.73, and 0.61 on the validation dataset and 0.59, 0.72, and 0.57 on the test dataset

Using denoising diffusion probabilistic models to enhance quality of limited-view photoacoustic tomography

In photoacoustic tomography (PAT), a limited angle of detector coverage around the object affects PAT image quality. Consequently, PAT images can become challenging to interpret accurately. Although deep learning methods, such as convolutional neural networks (CNNs), have shown promising results in recovering high-quality images from limited-view data, these methods suffer from loss of fine image details. Recently, denoising diffusion probabilistic models (DDPM) are gaining interest in image generation applications. Here we explore the potential of conditional denoising diffusion probabilistic models (cDDPM) to enhance quality of limited-view PAT images. The OADAT dataset consisting of 2D PAT images of healthy forearms acquired with a semicircle array of 256 ultrasound elements is used. PAT images are reconstructed using the full array (256 elements) and also the central 128, 64 and 32 elements for limited-view. The approach showed to be able to filter out limited-view streak artifacts, accurately recover shapes of vascular structures, and preserve fine-detailed texture. Conditional DDPMs show potential in improving quality of limited-view PAT reconstructions, however, they require higher computational cost compared to conventional CNNs. Future works will include the reduction of computational time and further evaluations on different datasets and array geometries.</p

Low-Cost, Continuous Motion Imaging, Computationally Augmented Whole Slide Imager for Digital Pathology

As digital pathology advances, systems for acquiring Whole Slide Images (WSI) need to become affordable and effective. Current research is majorly reported around the low-throughput stop-and-focus imaging systems that are too slow for clinical use. This work reports a system that acquires well focused images at a high throughput. It reduces the imaging time by an order of magnitude via the carefully engineered implementation of a global shutter camera which allows continuous motion imaging. The second challenge of maintaining good focus comes from three sources: the imperfections induced by low-cost optomechanics, smear surface undulations, and irregularity of the slide surface itself. These are minimized via a tiled-scan method supported by focal mapping, which predicts and achieves best focus on-the-fly. Finally, any remnant defocus is compensated for by a post-acquisition image enhancement method which sharpens the image without disturbing the essential features and color tones. Thus, this system delivers a 15 × 15 mm2 scan in about 3 minutes at a resolution of 0.78 μ m with a 40x microscopy objective. The bill of material cost is about US 1300 and hence it would be beneficial for telepathology in resource-constrained scenarios. Also, this device can serve as an enabler of Artificial Intelligence/Machine Learning algorithms to provide fully automated diagnosis. The developed codes for post-acquisition image enhancement are available at https://github.com/navchetan-awasthi/Microscopy

Vector extrapolation methods for accelerating iterative reconstruction methods in limited-data photoacoustic tomography

As limited data photoacoustic tomographic image reconstruction problem is known to be ill-posed, the iterative reconstruction methods were proven to be effective in terms of providing good quality initial pressure distribution. Often, these iterative methods require a large number of iterations to converge to a solution, in turn making the image reconstruction procedure computationally inefficient. In this work, two variants of vector polynomial extrapolation techniques were deployed to accelerate two standard iterative photoacoustic image reconstruction algorithms, including regularized steepest descent and total variation regularization methods. It is shown using numerical and experimental phantom cases that these extrapolation methods that are proposed in this work can provide significant acceleration (as high as 4.7 times) along with added advantage of improving reconstructed image quality. (c) 2018 Society of Photo-Optical Instrumentation Engineers (SPIE

Image-guided filtering for improving photoacoustic tomographic image reconstruction

Several algorithms exist to solve the photoacoustic image reconstruction problem depending on the expected reconstructed image features. These reconstruction algorithms promote typically one feature, such as being smooth or sharp, in the output image. Combining these features using a guided filtering approach was attempted in this work, which requires an input and guiding image. This approach act as a postprocessing step to improve commonly used Tikhonov or total variational regularization method. The result obtained from linear backprojection was used as a guiding image to improve these results. Using both numerical and experimental phantom cases, it was shown that the proposed guided filtering approach was able to improve (as high as 11.23 dB) the signal-to-noise ratio of the reconstructed images with the added advantage being computationally efficient. This approach was compared with state-of-the-art basis pursuit deconvolution as well as standard denoising methods and shown to outperform them. (C) 2018 Society of Photo-Optical Instrumentation Engineers (SPIE

A comprehensive review on efficient artificial intelligence models for classification of abnormal cardiac rhythms using electrocardiograms

Deep learning has made many advances in data classification using electrocardiogram (ECG) waveforms. Over the past decade, data science research has focused on developing artificial intelligence (AI) based models that can analyze ECG waveforms to identify and classify abnormal cardiac rhythms accurately. However, the primary drawback of the current AI models is that most of these models are heavy, computationally intensive, and inefficient in terms of cost for real-time implementation. In this review, we first discuss the current state-of-the-art AI models utilized for ECG-based cardiac rhythm classification. Next, we present some of the upcoming modeling methodologies which have the potential to perform real-time implementation of AI-based heart rhythm diagnosis. These models hold significant promise in being lightweight and computationally efficient without compromising the accuracy. Contemporary models predominantly utilize 12-lead ECG for cardiac rhythm classification and cardiovascular status prediction, increasing the computational burden and making real-time implementation challenging. We also summarize research studies evaluating the potential of efficient data setups to reduce the number of ECG leads without affecting classification accuracy. Lastly, we present future perspectives on AI's utility in precision medicine by providing opportunities for accurate prediction and diagnostics of cardiovascular status in patients

Deep neural network-based sinogram super-resolution and bandwidth enhancement for limited-data photoacoustic tomography

Photoacoustic tomography (PAT) is a noninvasive imaging modality combining the benefits of optical contrast at ultrasonic resolution. Analytical reconstruction algorithms for photoacoustic (PA) signals require a large number of data points for accurate image reconstruction. However, in practical scenarios, data are collected using the limited number of transducers along with data being often corrupted with noise resulting in only qualitative images. Furthermore, the collected boundary data are band-limited due to limited bandwidth (BW) of the transducer, making the PA imaging with limited data being qualitative. In this work, a deep neural network-based model with loss function being scaled root-mean-squared error was proposed for super-resolution, denoising, as well as BW enhancement of the PA signals collected at the boundary of the domain. The proposed network has been compared with traditional as well as other popular deep-learning methods in numerical as well as experimental cases and is shown to improve the collected boundary data, in turn, providing superior quality reconstructed PA image. The improvement obtained in the Pearson correlation, structural similarity index metric, and root-mean-square error was as high as 35.62%, 33.81%, and 41.07%, respectively, for phantom cases and signal-to-noise ratio improvement in the reconstructed PA images was as high as 11.65 dB for in vivo cases compared with reconstructed image obtained using original limited BW data. Code is available at https://sites.google.com/site/sercmig/home/dnnpat.Accepted versio