Special Issue "Deep Learning in Medical Image Analysis, Volume II"

A special issue of Journal of Imaging (ISSN 2313-433X). This special issue belongs to the section "Medical Imaging".

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 4920

Special Issue Editors

Prof. Dr. Yudong Zhang
grade E-Mail Website
Guest Editor
School of Computer Science, University of Leicester, Leicester LE1 7RH, UK
Interests: deep learning; convolutional neural network; graph convolutional network; attention network; explainable AI; biomedical image analysis; bio-inspired computing; pattern recognition; transfer learning; medical sensor
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Juan Manuel Gorriz
E-Mail Website
Guest Editor
Department of Signal Theory, Telematics and Communications, University of Granada, 18071 Granada, Spain
Interests: artificial intelligence; statistical signal processing; biomedical applications; machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Zhengchao Dong
E-Mail Website
Guest Editor
1. Molecular Imaging and Neuropathology Division, Columbia University and New York State Psychiatric Institute, New York, NY 10032, USA
2. New York State Psychiatric Institute, New York, NY 10032, USA
Interests: structural mechanics; computational mechanics; contact mechanics; efficient solvers; interfaces; modeling; applications in mechanical and civil engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the past few years, deep learning has established itself as a powerful tool across a broad spectrum of domains in imaging, e.g., classification, prediction, detection, segmentation, diagnosis, interpretation, and reconstruction. While deep neural networks initially found nurture in the computer vision community, they have quickly spread over medical imaging applications.

The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and in understanding the underlying biological process.

The purpose of this Special Issue on “Deep Learning in Medical Image Analysis, Volume II” is to present and highlight novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis.

Prof. Dr. Yudong Zhang
Prof. Dr. Juan Manuel Gorriz
Prof. Dr. Zhengchao Dong
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Imaging is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • deep learning
  • transfer learning
  • deep neural network
  • convolutional neural network
  • graph neural network
  • multitask learning
  • explainable AI
  • attention mechanism
  • biomedical engineering
  • multimodal imaging
  • semantic segmentation
  • image reconstruction
  • healthcare

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Published Papers (4 papers)

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Research

Article
Comparison of Ultrasound Image Classifier Deep Learning Algorithms for Shrapnel Detection
J. Imaging 2022, 8(5), 140; https://doi.org/10.3390/jimaging8050140 - 20 May 2022
Viewed by 516
Abstract
Ultrasound imaging is essential in emergency medicine and combat casualty care, oftentimes used as a critical triage tool. However, identifying injuries, such as shrapnel embedded in tissue or a pneumothorax, can be challenging without extensive ultrasonography training, which may not be available in [...] Read more.
Ultrasound imaging is essential in emergency medicine and combat casualty care, oftentimes used as a critical triage tool. However, identifying injuries, such as shrapnel embedded in tissue or a pneumothorax, can be challenging without extensive ultrasonography training, which may not be available in prolonged field care or emergency medicine scenarios. Artificial intelligence can simplify this by automating image interpretation but only if it can be deployed for use in real time. We previously developed a deep learning neural network model specifically designed to identify shrapnel in ultrasound images, termed ShrapML. Here, we expand on that work to further optimize the model and compare its performance to that of conventional models trained on the ImageNet database, such as ResNet50. Through Bayesian optimization, the model’s parameters were further refined, resulting in an F1 score of 0.98. We compared the proposed model to four conventional models: DarkNet-19, GoogleNet, MobileNetv2, and SqueezeNet which were down-selected based on speed and testing accuracy. Although MobileNetv2 achieved a higher accuracy than ShrapML, there was a tradeoff between accuracy and speed, with ShrapML being 10× faster than MobileNetv2. In conclusion, real-time deployment of algorithms such as ShrapML can reduce the cognitive load for medical providers in high-stress emergency or miliary medicine scenarios. Full article
(This article belongs to the Special Issue Deep Learning in Medical Image Analysis, Volume II)
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Article
Addressing Motion Blurs in Brain MRI Scans Using Conditional Adversarial Networks and Simulated Curvilinear Motions
J. Imaging 2022, 8(4), 84; https://doi.org/10.3390/jimaging8040084 - 23 Mar 2022
Viewed by 840
Abstract
In-scanner head motion often leads to degradation in MRI scans and is a major source of error in diagnosing brain abnormalities. Researchers have explored various approaches, including blind and nonblind deconvolutions, to correct the motion artifacts in MRI scans. Inspired by the recent [...] Read more.
In-scanner head motion often leads to degradation in MRI scans and is a major source of error in diagnosing brain abnormalities. Researchers have explored various approaches, including blind and nonblind deconvolutions, to correct the motion artifacts in MRI scans. Inspired by the recent success of deep learning models in medical image analysis, we investigate the efficacy of employing generative adversarial networks (GANs) to address motion blurs in brain MRI scans. We cast the problem as a blind deconvolution task where a neural network is trained to guess a blurring kernel that produced the observed corruption. Specifically, our study explores a new approach under the sparse coding paradigm where every ground truth corrupting kernel is assumed to be a “combination” of a relatively small universe of “basis” kernels. This assumption is based on the intuition that, on small distance scales, patients’ moves follow simple curves and that complex motions can be obtained by combining a number of simple ones. We show that, with a suitably dense basis, a neural network can effectively guess the degrading kernel and reverse some of the damage in the motion-affected real-world scans. To this end, we generated 10,000 continuous and curvilinear kernels in random positions and directions that are likely to uniformly populate the space of corrupting kernels in real-world scans. We further generated a large dataset of 225,000 pairs of sharp and blurred MR images to facilitate training effective deep learning models. Our experimental results demonstrate the viability of the proposed approach evaluated using synthetic and real-world MRI scans. Our study further suggests there is merit in exploring separate models for the sagittal, axial, and coronal planes. Full article
(This article belongs to the Special Issue Deep Learning in Medical Image Analysis, Volume II)
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Article
Per-COVID-19: A Benchmark Dataset for COVID-19 Percentage Estimation from CT-Scans
J. Imaging 2021, 7(9), 189; https://doi.org/10.3390/jimaging7090189 - 18 Sep 2021
Cited by 1 | Viewed by 1522
Abstract
COVID-19 infection recognition is a very important step in the fight against the COVID-19 pandemic. In fact, many methods have been used to recognize COVID-19 infection including Reverse Transcription Polymerase Chain Reaction (RT-PCR), X-ray scan, and Computed Tomography scan (CT- scan). In addition [...] Read more.
COVID-19 infection recognition is a very important step in the fight against the COVID-19 pandemic. In fact, many methods have been used to recognize COVID-19 infection including Reverse Transcription Polymerase Chain Reaction (RT-PCR), X-ray scan, and Computed Tomography scan (CT- scan). In addition to the recognition of the COVID-19 infection, CT scans can provide more important information about the evolution of this disease and its severity. With the extensive number of COVID-19 infections, estimating the COVID-19 percentage can help the intensive care to free up the resuscitation beds for the critical cases and follow other protocol for less severity cases. In this paper, we introduce COVID-19 percentage estimation dataset from CT-scans, where the labeling process was accomplished by two expert radiologists. Moreover, we evaluate the performance of three Convolutional Neural Network (CNN) architectures: ResneXt-50, Densenet-161, and Inception-v3. For the three CNN architectures, we use two loss functions: MSE and Dynamic Huber. In addition, two pretrained scenarios are investigated (ImageNet pretrained models and pretrained models using X-ray data). The evaluated approaches achieved promising results on the estimation of COVID-19 infection. Inception-v3 using Dynamic Huber loss function and pretrained models using X-ray data achieved the best performance for slice-level results: 0.9365, 5.10, and 9.25 for Pearson Correlation coefficient (PC), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE), respectively. On the other hand, the same approach achieved 0.9603, 4.01, and 6.79 for PCsubj, MAEsubj, and RMSEsubj, respectively, for subject-level results. These results prove that using CNN architectures can provide accurate and fast solution to estimate the COVID-19 infection percentage for monitoring the evolution of the patient state. Full article
(This article belongs to the Special Issue Deep Learning in Medical Image Analysis, Volume II)
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Article
How Can a Deep Learning Algorithm Improve Fracture Detection on X-rays in the Emergency Room?
J. Imaging 2021, 7(7), 105; https://doi.org/10.3390/jimaging7070105 - 25 Jun 2021
Cited by 1 | Viewed by 1252
Abstract
The growing need for emergency imaging has greatly increased the number of conventional X-rays, particularly for traumatic injury. Deep learning (DL) algorithms could improve fracture screening by radiologists and emergency room (ER) physicians. We used an algorithm developed for the detection of appendicular [...] Read more.
The growing need for emergency imaging has greatly increased the number of conventional X-rays, particularly for traumatic injury. Deep learning (DL) algorithms could improve fracture screening by radiologists and emergency room (ER) physicians. We used an algorithm developed for the detection of appendicular skeleton fractures and evaluated its performance for detecting traumatic fractures on conventional X-rays in the ER, without the need for training on local data. This algorithm was tested on all patients (N = 125) consulting at the Louis Mourier ER in May 2019 for limb trauma. Patients were selected by two emergency physicians from the clinical database used in the ER. Their X-rays were exported and analyzed by a radiologist. The prediction made by the algorithm and the annotation made by the radiologist were compared. For the 125 patients included, 25 patients with a fracture were identified by the clinicians, 24 of whom were identified by the algorithm (sensitivity of 96%). The algorithm incorrectly predicted a fracture in 14 of the 100 patients without fractures (specificity of 86%). The negative predictive value was 98.85%. This study shows that DL algorithms are potentially valuable diagnostic tools for detecting fractures in the ER and could be used in the training of junior radiologists. Full article
(This article belongs to the Special Issue Deep Learning in Medical Image Analysis, Volume II)
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