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Life
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Artificial Intelligence Applications for Imaging in Life Sciences
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Artificial Intelligence Applications for Imaging in Life Sciences

A special issue of Life (ISSN 2075-1729). This special issue belongs to the section "Radiobiology and Nuclear Medicine".

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 16168

Special Issue Editors

Dr. Sandro Donato

E-Mail Website
Guest Editor
Department of Physics, University of Calabria, 87036 Rende, CS, Italy
Interests: X-ray computed micro-tomography; image processing; segmentation; X-ray detectors; synchrotron radiation
Dr. Pierangela Bruno

E-Mail Website
Guest Editor
Department of Mathematics and Computer Science, University of Calabria, 87036 Rende, CS, Italy
Interests: artificial intelligence; deep learning; neural networks; medical image analysis

Special Issue Information

Dear Colleagues,

In the last few decades, imaging has become an essential part of scientific research, particularly in life sciences. Many breakthroughs have been achieved through imaging. At the same time, the volume of visual data produced is growing dramatically by the day, as is the need for more and more powerful computational resources and advanced methods to enable quantitative image analysis, possibly in an automated way. Image data contain valuable information that can be overlooked, lost, or misinterpreted. The solution found by scientists around the world is to let an intelligent machine perform this task. Artificial Intelligence (AI) approaches—i.e., computer systems able to mimic human intelligence—have gained special attention in the scientific community thanks to their ability to recognize patterns, analyze huge amounts of data, and discover non-trivial functional relationships between inputs and outputs. These approaches (in particular, those based on machine learning and deep learning), are widely used and integrated into an increasing number of domains in life sciences. Over a time span of just a few years, the scientific literature on AI has seen a surge, with not only reviews and surveys exploring and commenting on the state of the art in this area but also with research papers describing specific applications of bioimaging.

This Special Issue will collect both review articles and, more particularly, original papers based on applications of AI in the context of imaging for life sciences. The topics of interest for this Special Issue include, but are not limited to, the following:

  • AI-based image segmentation;
  • Image classification;
  • Object detection in bioimages;
  • Explainable Artificial Intelligence in bioimaging;
  • Deep learning in bioimage analysis;
  • Deep learning for bioimage generation.

Dr. Sandro Donato
Dr. Pierangela Bruno
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. Life 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 2600 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

  • imaging in life sciences
  • machine learning
  • deep learning
  • computer vision
  • image segmentation
  • classification

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Published Papers (5 papers)

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Research

Jump to: Review

26 pages, 30853 KiB  
Article
Automatic Differentiation for Inverse Problems in X-ray Imaging and Microscopy
by Francesco Guzzi, Alessandra Gianoncelli, Fulvio Billè, Sergio Carrato and George Kourousias
Life 2023, 13(3), 629; https://doi.org/10.3390/life13030629 - 23 Feb 2023
Cited by 7 | Viewed by 2524
Abstract
Computational techniques allow breaking the limits of traditional imaging methods, such as time restrictions, resolution, and optics flaws. While simple computational methods can be enough for highly controlled microscope setups or just for previews, an increased level of complexity is instead required for [...] Read more.
Computational techniques allow breaking the limits of traditional imaging methods, such as time restrictions, resolution, and optics flaws. While simple computational methods can be enough for highly controlled microscope setups or just for previews, an increased level of complexity is instead required for advanced setups, acquisition modalities or where uncertainty is high; the need for complex computational methods clashes with rapid design and execution. In all these cases, Automatic Differentiation, one of the subtopics of Artificial Intelligence, may offer a functional solution, but only if a GPU implementation is available. In this paper, we show how a framework built to solve just one optimisation problem can be employed for many different X-ray imaging inverse problems. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications for Imaging in Life Sciences)
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11 pages, 3072 KiB  
Article
Monitoring Methodology for an AI Tool for Breast Cancer Screening Deployed in Clinical Centers
by Carlos Aguilar, Serena Pacilè, Nicolas Weber and Pierre Fillard
Life 2023, 13(2), 440; https://doi.org/10.3390/life13020440 - 4 Feb 2023
Cited by 2 | Viewed by 2103
Abstract
We propose a methodology for monitoring an artificial intelligence (AI) tool for breast cancer screening when deployed in clinical centers. An AI trained to detect suspicious regions of interest in the four views of a mammogram and to characterize their level of suspicion [...] Read more.
We propose a methodology for monitoring an artificial intelligence (AI) tool for breast cancer screening when deployed in clinical centers. An AI trained to detect suspicious regions of interest in the four views of a mammogram and to characterize their level of suspicion with a score ranging from one (low suspicion) to ten (high suspicion of malignancy) was deployed in four radiological centers across the US. Results were collected between April 2021 and December 2022, resulting in a dataset of 36,581 AI records. To assess the behavior of the AI, its score distribution in each center was compared to a reference distribution obtained in silico using the Pearson correlation coefficient (PCC) between each center AI score distribution and the reference. The estimated PCCs were 0.998 [min: 0.993, max: 0.999] for center US-1, 0.975 [min: 0.923, max: 0.986] for US-2, 0.995 [min: 0.972, max: 0.998] for US-3 and 0.994 [min: 0.962, max: 0.982] for US-4. These values show that the AI behaved as expected. Low PCC values could be used to trigger an alert, which would facilitate the detection of software malfunctions. This methodology can help create new indicators to improve monitoring of software deployed in hospitals. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications for Imaging in Life Sciences)
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14 pages, 2906 KiB  
Article
A Robust Computer-Aided Automated Brain Tumor Diagnosis Approach Using PSO-ReliefF Optimized Gaussian and Non-Linear Feature Space
by Muhammad Umair Ali, Karam Dad Kallu, Haris Masood, Shaik Javeed Hussain, Safee Ullah, Jong Hyuk Byun, Amad Zafar and Kawang Su Kim
Life 2022, 12(12), 2036; https://doi.org/10.3390/life12122036 - 6 Dec 2022
Cited by 4 | Viewed by 2885
Abstract
Brain tumors are among the deadliest diseases in the modern world. This study proposes an optimized machine-learning approach for the detection and identification of the type of brain tumor (glioma, meningioma, or pituitary tumor) in brain images recorded using magnetic resonance imaging (MRI). [...] Read more.
Brain tumors are among the deadliest diseases in the modern world. This study proposes an optimized machine-learning approach for the detection and identification of the type of brain tumor (glioma, meningioma, or pituitary tumor) in brain images recorded using magnetic resonance imaging (MRI). The Gaussian features of the image are extracted using speed-up robust features (SURF), whereas its non-linear features are obtained using KAZE, owing to their high performance against rotation, scaling, and noise problems. To retrieve local-level information, all brain MRI images are segmented into an 8 × 8 pixel grid. To enhance the accuracy and reduce the computational time, the variance-based k-means clustering and PSO-ReliefF algorithms are employed to eliminate the redundant features of the brain MRI images. Finally, the performance of the proposed hybrid optimized feature vector is evaluated using various machine learning classifiers. An accuracy of 96.30% is obtained with 169 features using a support vector machine (SVM). Furthermore, the computational time is also reduced to 1 min compared to the non-optimized features used for training of the SVM. The findings are also compared with previous research, demonstrating that the suggested approach might assist physicians and doctors in the timely detection of brain tumors. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications for Imaging in Life Sciences)
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18 pages, 3959 KiB  
Article
RNN and BiLSTM Fusion for Accurate Automatic Epileptic Seizure Diagnosis Using EEG Signals
by Nagwan Abdel Samee, Noha F. Mahmoud, Eman A. Aldhahri, Ahsan Rafiq, Mohammed Saleh Ali Muthanna and Ijaz Ahmad
Life 2022, 12(12), 1946; https://doi.org/10.3390/life12121946 - 22 Nov 2022
Cited by 17 | Viewed by 2772
Abstract
Epilepsy is a common neurological condition. The effects of epilepsy are not restricted to seizures alone. They comprise a wide spectrum of problems that might impair and reduce quality of life. Even with medication, 30% of epilepsy patients still have recurring seizures. An [...] Read more.
Epilepsy is a common neurological condition. The effects of epilepsy are not restricted to seizures alone. They comprise a wide spectrum of problems that might impair and reduce quality of life. Even with medication, 30% of epilepsy patients still have recurring seizures. An epileptic seizure is caused by significant neuronal electrical activity, which affects brain activity. EEG shows these changes as high-amplitude spiky and sluggish waves. Recognizing seizures on an electroencephalogram (EEG) manually by a professional neurologist is a time-consuming and labor-intensive process, hence an efficient automated approach is necessary for the identification of epileptic seizure. One technique to increase the speed and accuracy with which a diagnosis of epileptic seizures could be made is by utilizing computer-aided diagnosis systems that are built on deep neural networks, or DNN. This study introduces a fusion of recurrent neural networks (RNNs) and bi-directional long short-term memories (BiLSTMs) for automatic epileptic seizure identification via EEG signal processing in order to tackle the aforementioned informational challenges. An electroencephalogram’s (EEG) raw data were first normalized after undergoing pre-processing. A RNN model was fed the normalized EEG sequence data and trained to accurately extract features from the data. Afterwards, the features were passed to the BiLSTM layers for processing so that further temporal information could be retrieved. In addition, the proposed RNN-BiLSTM model was tested in an experimental setting using the freely accessible UCI epileptic seizure dataset. Experimental findings of the suggested model have achieved avg values of 98.90%, 98.50%, 98. 20%, and 98.60%, respectively, for accuracy, sensitivity, precision, and specificity. To further verify the new model’s efficacy, it is compared to other models, such as the RNN-LSTM and the RNN-GRU learning models, and is shown to have improved the same metrics by 1.8%, 1.69%, 1.95%, and 2.2% on using 5-fold. Additionally, the proposed method was compared to state-of-the-art approaches and proved to be a more accurate categorization of such techniques. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications for Imaging in Life Sciences)
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Review

Jump to: Research

20 pages, 3245 KiB  
Review
Deep Learning for Echocardiography: Introduction for Clinicians and Future Vision: State-of-the-Art Review
by Chayakrit Krittanawong, Alaa Mabrouk Salem Omar, Sukrit Narula, Partho P. Sengupta, Benjamin S. Glicksberg, Jagat Narula and Edgar Argulian
Life 2023, 13(4), 1029; https://doi.org/10.3390/life13041029 - 17 Apr 2023
Cited by 8 | Viewed by 4598
Abstract
Exponential growth in data storage and computational power is rapidly narrowing the gap between translating findings from advanced clinical informatics into cardiovascular clinical practice. Specifically, cardiovascular imaging has the distinct advantage in providing a great quantity of data for potentially rich insights, but [...] Read more.
Exponential growth in data storage and computational power is rapidly narrowing the gap between translating findings from advanced clinical informatics into cardiovascular clinical practice. Specifically, cardiovascular imaging has the distinct advantage in providing a great quantity of data for potentially rich insights, but nuanced interpretation requires a high-level skillset that few individuals possess. A subset of machine learning, deep learning (DL), is a modality that has shown promise, particularly in the areas of image recognition, computer vision, and video classification. Due to a low signal-to-noise ratio, echocardiographic data tend to be challenging to classify; however, utilization of robust DL architectures may help clinicians and researchers automate conventional human tasks and catalyze the extraction of clinically useful data from the petabytes of collected imaging data. The promise is extending far and beyond towards a contactless echocardiographic exam—a dream that is much needed in this time of uncertainty and social distancing brought on by a stunning pandemic culture. In the current review, we discuss state-of-the-art DL techniques and architectures that can be used for image and video classification, and future directions in echocardiographic research in the current era. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications for Imaging in Life Sciences)
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