U-net is an image segmentation technique developed primarily for image segmentation tasks. These traits provide U-net with a high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in nearly all major image modalities, from CT scans and MRI to X-rays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. Given that U-net’s potential is still increasing, this narrative literature review examines the numerous developments and breakthroughs in the U-net architecture and provides observations on recent trends. We also discuss the many innovations that have advanced in deep learning and discuss how these tools facilitate U-net. In addition, we review the different image modalities and application areas that have been enhanced by U-net.

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

SARS-CoV-2 has infected over ∼165 million people worldwide causing Acute Respiratory Distress Syndrome (ARDS) and has killed ∼3.4 million people. Artificial Intelligence (AI) has shown to benefit in the biomedical image such as X-ray/Computed Tomography in diagnosis of ARDS, but there are limited AI-based systematic reviews (aiSR). The purpose of this study is to understand the Risk-of-Bias (RoB) in a non-randomized AI trial for handling ARDS using novel AtheroPoint-AI-Bias (AP(ai)Bias). Our hypothesis for acceptance of a study to be in low RoB must have a mean score of 80% in a study. Using the PRISMA model, 42 best AI studies were analyzed to understand the RoB. Using the AP(ai)Bias paradigm, the top 19 studies were then chosen using the raw-cutoff of 1.9. This was obtained using the intersection of the cumulative plot of “mean score vs. study” and score distribution. Finally, these studies were benchmarked against ROBINS-I and PROBAST paradigm. Our observation showed that AP(ai)Bias, ROBINS-I, and PROBAST had only 32%, 16%, and 26% studies, respectively in low-moderate RoB (cutoff>2.5), however none of them met the RoB hypothesis. Further, the aiSR analysis recommends six primary and six secondary recommendations for the non-randomized AI for ARDS. The primary recommendations for improvement in AI-based ARDS design inclusive of (i) comorbidity, (ii) inter-and intra-observer variability studies, (iii) large data size, (iv) clinical validation, (v) granularity of COVID-19 risk, and (vi) cross-modality scientific validation. The AI is an important component for diagnosis of ARDS and the recommendations must be followed to lower the RoB.

Systematic Review of Artificial Intelligence in Acute Respiratory Distress Syndrome for COVID-19 Lung Patients: A Biomedical Imaging Perspective

A new deep learning method for blood vessel segmentation in retinal images based on convolutional kernels and modified U-Net model

Alzheimer’s disease (AD) is the most common type of dementia that still has no effective treatment. Accurate classification of AD can help in its diagnosis and selection of the most effective treatment options. In the last decade, several studies have proven the effectiveness of deep learning algorithms for AD diagnosis. In this paper, we propose a U-net style model for AD diagnosis using 3D T1-weighted magnetic resonance images (MRI). Combining with deep supervision has been proved to be effective in improving the performance of the model. Our method has been tested on a subset of ADNI dataset and AIBL dataset and achieves a superior average accuracy of $$95.71\pm 1.36\%$$
 for AD versus NC (normal control), $$90.14\pm 3.66\%$$
 for EMCI (early mild cognitive impairment) versus LMCI (late mild cognitive impairment), $$90.05\pm 2.63\%$$
 for AD versus LMCI, and $$87.98\pm 4.54\%$$
 for NC versus EMCI, respectively. Besides these binary-classification tasks, we also test this model for multi-class classification task (AD vs. NC vs. EMCI vs. LMCI) and it achieves an accuracy of $$86.47\pm 9.60\%$$
 . Furthermore, 3D-Grad-CAM method is used to visualize the focused areas of the proposed model. We find that the proposed model pays more attention to the characteristics of the ventricles, hippocampus, and some regions of cortex, which have been proven to be affected by AD.

U-net based analysis of MRI for Alzheimer’s disease diagnosis

Brain tumors are the growth of abnormal cells or a mass in a brain. Numerous kinds of brain tumors were discovered, which need accurate and early detection techniques. Currently, most diagnosis and detection methods rely on the decision of neuro-specialists and radiologists to evaluate brain images, which may be time-consuming and cause human errors. This paper proposes a robust U-Net deep learning Convolutional Neural Network (CNN) model that can classify if the subject has a tumor or not based on Brain Magnetic resonance imaging (MRI) with acceptable accuracy for medical-grade application. The study built and trained the 3D U-Net CNN including encoding/decoding relationship architecture to perform the brain tumor segmentation because it requires fewer training images and provides more precise segmentation. The algorithm consists of three parts; the first part, the downsampling part, the bottleneck part, and the optimum part. The resultant semantic maps are inserted into the decoder fraction to obtain the full-resolution probability maps. The developed U-Net architecture has been applied on the MRI scan brain tumor segmentation dataset in MICCAI BraTS 2017. The results using Matlab-based toolbox indicate that the proposed architecture has been successfully evaluated and experienced for MRI datasets of brain tumor segmentation including 336 images as training data and 125 images for validation. This work demonstrated comparative performance and successful feasibility of implementing U-Net CNN architecture in an automated framework of brain tumor segmentations in Fluid-attenuated inversion recovery (FLAIR) MR Slices. The developed U-Net CNN model succeeded in performing the brain tumor segmentation task to classify the input brain images into a tumor or not based on the MRI dataset.

/pdf/development-of-brain-tumor-segmentation-of-magnetic-4bni0d1sgp.pdf

Development of brain tumor segmentation of magnetic resonance imaging (MRI) using U-Net deep learning

Background: Accurate and timely diagnosis and effective prognosis of the disease is important to provide the best possible care for patients with COVID-19 and reduce the burden on the health care system. Machine learning methods can play a vital role in the diagnosis of COVID-19 by processing chest x-ray images.
Objective: The aim of this study is to summarize information on the use of intelligent models for the diagnosis and prognosis of COVID-19 to help with early and timely diagnosis, minimize prolonged diagnosis, and improve overall health care.
Methods: A systematic search of databases, including PubMed, Web of Science, IEEE, ProQuest, Scopus, bioRxiv, and medRxiv, was performed for COVID-19–related studies published up to May 24, 2020. This study was performed in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. All original research articles describing the application of image processing for the prediction and diagnosis of COVID-19 were considered in the analysis. Two reviewers independently assessed the published papers to determine eligibility for inclusion in the analysis. Risk of bias was evaluated using the Prediction Model Risk of Bias Assessment Tool.
Results: Of the 629 articles retrieved, 44 articles were included. We identified 4 prognosis models for calculating prediction of disease severity and estimation of confinement time for individual patients, and 40 diagnostic models for detecting COVID-19 from normal or other pneumonias. Most included studies used deep learning methods based on convolutional neural networks, which have been widely used as a classification algorithm. The most frequently reported predictors of prognosis in patients with COVID-19 included age, computed tomography data, gender, comorbidities, symptoms, and laboratory findings. Deep convolutional neural networks obtained better results compared with non–neural network–based methods. Moreover, all of the models were found to be at high risk of bias due to the lack of information about the study population, intended groups, and inappropriate reporting.
Conclusions: Machine learning models used for the diagnosis and prognosis of COVID-19 showed excellent discriminative performance. However, these models were at high risk of bias, because of various reasons such as inadequate information about study participants, randomization process, and the lack of external validation, which may have resulted in the optimistic reporting of these models. Hence, our findings do not recommend any of the current models to be used in practice for the diagnosis and prognosis of COVID-19.

Machine Learning Models for Image-Based Diagnosis and Prognosis of COVID-19: Systematic Review.

Since medical imaging is a fundamental step in clinical diagnosis and treatment, medical image processing is an attractive field for researchers. Among the different applications of medical image processing, this paper focuses on the segmentation task using a customized deep convolutional neural network (CNN).
 The proposed network is developed based on the idea of improving a deep network performance and speeding up its learning process while using less parameters. Using the famous U-Net architecture which has proven its effectiveness in the segmentation field, the customization is done here by applying adaptive activation functions. In the proposed network, the U-Net complexity is reduced about 200 times by alleviating some parameters to accelerate the learning process. However, the important modification is the use of adaptive activation functions where each convolution layer learns its own data-adaptive activation function as a linear combination of 16 well-known basic functions. This modification successfully compensates the accuracy drop caused by parameter alleviation and also makes the model capable to be tuned with small amount of training data. Conducting several experiments on five famous retinal image datasets, namely DRIVE, STARE, CHASE, HRF, and ARIA, the proposed customized U-Net achieved 96%, 97%, 96%, 97%, and 95% accuracy in segmenting blood vessels, respectively. The proposed network also showed 98% accuracy on ISIC skin lesion dataset for segmenting the lesion area. The obtained results obviously confirm the high performance of the proposed customized network compared to the previous successful researches in handling the medical segmentation task. They also light the hope that many famous deep networks can benefit from these types of customization to become efficient compact models with the ability to handle lack of sufficient data.

Medical image segmentation using customized U-Net with adaptive activation functions

/pdf/multi-pose-face-recognition-using-pairwise-supervised-3rynkw9zy5.pdf

Ali Farahani

Papers

Machine Learning Models for Image-Based Diagnosis and Prognosis of COVID-19: Systematic Review.

Medical image segmentation using customized U-Net with adaptive activation functions

Multi-Pose Face Recognition Using Pairwise Supervised Dictionary Learning