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Deep Learning and Data Analytics Techniques for Processing of Biomedical Images

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

Deadline for manuscript submissions: closed (1 August 2023) | Viewed by 10269

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Special Issue Editors

Dr. Sathishkumar Veerappampalayam Easwaramoorthy

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Guest Editor

Department of Industrial Engineering, Hanyang University, 222 Wangsimini-ro, Seongdong-gu, Seoul 04763, Republic of Korea
Interests: data mining; machine learning; big data analytics; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Dr. Neelakandan Subramani

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Guest Editor

Department of CSE, R.M.K Engineering College, Chennai, India
Interests: data science; deep learning and machine learning

Special Issue Information

Dear Colleagues,

Research on computer analysis of medical images holds great potential for enhancing the health of patients. However, a number of systematic obstacles are impeding the field’s advancement, including data limitations, such as biases, and research incentives, such as optimization for publication. Medical imaging plays a significant role in different clinical applications, such as medical procedures used for early detection, monitoring, diagnosis, and treatment evaluation of various medical conditions. The basics of the principles and implementations of artificial neural networks and deep learning are essential for understanding medical image analysis in computer vision. The deep learning approach (DLA) in medical image analysis has emerged as a fast-growing research field. Deep learning has recently revolutionized medical image computing methods by automating the discovery of features and producing superior results. Recent developments in deep learning have heightened the importance of biomedical signal and image processing research. In order to provide clinicians with useful information, biomedical signal processing requires the analysis of measurements taken at specific points in time and recorded in a patient’s chart. Biomedical image processing is conceptually similar to biomedical signal processing in multiple dimensions. Using X-ray, ultrasound, MRI, nuclear medicine, and visual imaging technologies, it involves image analysis, enhancement, and presentation.

In response, this Special Issue solicits original and novel methodological contributions addressing key challenges in the explainability and generalizability of deep learning for medical imaging. Submissions should emphasize research and advanced development of technical aspects of new image analysis methodologies, and all newly developed methods should be evaluated or validated using real and massive medical imaging data.

Dr. Sathishkumar V E.
Dr. Neelakandan Subramani
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 1800 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

deep learning
internet of things
internet of medical things
biomedical image analysis
medical image processing
medical disease analysis
biomedical data analytics
multimodal image analysis
healthcare data analysis

Published Papers (5 papers)

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Research

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11 pages, 2472 KiB

Open AccessArticle

Synthetic Megavoltage Cone Beam Computed Tomography Image Generation for Improved Contouring Accuracy of Cardiac Pacemakers

by Hana Baroudi, Xinru Chen, Wenhua Cao, Mohammad D. El Basha, Skylar Gay, Mary Peters Gronberg, Soleil Hernandez, Kai Huang, Zaphanlene Kaffey, Adam D. Melancon, Raymond P. Mumme, Carlos Sjogreen, January Y. Tsai, Cenji Yu, Laurence E. Court, Ramiro Pino and Yao Zhao

J. Imaging 2023, 9(11), 245; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging9110245 - 08 Nov 2023

Viewed by 1553

Abstract

In this study, we aimed to enhance the contouring accuracy of cardiac pacemakers by improving their visualization using deep learning models to predict MV CBCT images based on kV CT or CBCT images. Ten pacemakers and four thorax phantoms were included, creating a total of 35 combinations. Each combination was imaged on a Varian Halcyon (kV/MV CBCT images) and Siemens SOMATOM CT scanner (kV CT images). Two generative adversarial network (GAN)-based models, cycleGAN and conditional GAN (cGAN), were trained to generate synthetic MV (sMV) CBCT images from kV CT/CBCT images using twenty-eight datasets (80%). The pacemakers in the sMV CBCT images and original MV CBCT images were manually delineated and reviewed by three users. The Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and mean surface distance (MSD) were used to compare contour accuracy. Visual inspection showed the improved visualization of pacemakers on sMV CBCT images compared to original kV CT/CBCT images. Moreover, cGAN demonstrated superior performance in enhancing pacemaker visualization compared to cycleGAN. The mean DSC, HD95, and MSD for contours on sMV CBCT images generated from kV CT/CBCT images were 0.91 ± 0.02/0.92 ± 0.01, 1.38 ± 0.31 mm/1.18 ± 0.20 mm, and 0.42 ± 0.07 mm/0.36 ± 0.06 mm using the cGAN model. Deep learning-based methods, specifically cycleGAN and cGAN, can effectively enhance the visualization of pacemakers in thorax kV CT/CBCT images, therefore improving the contouring precision of these devices. Full article

(This article belongs to the Special Issue Deep Learning and Data Analytics Techniques for Processing of Biomedical Images)

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15 pages, 2442 KiB

Open AccessArticle

Limitations of Out-of-Distribution Detection in 3D Medical Image Segmentation

by Anton Vasiliuk, Daria Frolova, Mikhail Belyaev and Boris Shirokikh

J. Imaging 2023, 9(9), 191; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging9090191 - 18 Sep 2023

Cited by 1 | Viewed by 1527

Abstract

Deep learning models perform unreliably when the data come from a distribution different from the training one. In critical applications such as medical imaging, out-of-distribution (OOD) detection methods help to identify such data samples, preventing erroneous predictions. In this paper, we further investigate OOD detection effectiveness when applied to 3D medical image segmentation. We designed several OOD challenges representing clinically occurring cases and found that none of the methods achieved acceptable performance. Methods not dedicated to segmentation severely failed to perform in the designed setups; the best mean false-positive rate at a 95% true-positive rate (FPR) was 0.59. Segmentation-dedicated methods still achieved suboptimal performance, with the best mean FPR being 0.31 (lower is better). To indicate this suboptimality, we developed a simple method called Intensity Histogram Features (IHF), which performed comparably or better in the same challenges, with a mean FPR of 0.25. Our findings highlight the limitations of the existing OOD detection methods with 3D medical images and present a promising avenue for improving them. To facilitate research in this area, we release the designed challenges as a publicly available benchmark and formulate practical criteria to test the generalization of OOD detection beyond the suggested benchmark. We also propose IHF as a solid baseline to contest emerging methods. Full article

(This article belongs to the Special Issue Deep Learning and Data Analytics Techniques for Processing of Biomedical Images)

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14 pages, 3796 KiB

Open AccessArticle

A Deep Learning-Based Model for Classifying Osteoporotic Lumbar Vertebral Fractures on Radiographs: A Retrospective Model Development and Validation Study

by Yohei Ono, Nobuaki Suzuki, Ryosuke Sakano, Yasuka Kikuchi, Tasuku Kimura, Kenneth Sutherland and Tamotsu Kamishima

J. Imaging 2023, 9(9), 187; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging9090187 - 18 Sep 2023

Viewed by 1132

Abstract

Early diagnosis and initiation of treatment for fresh osteoporotic lumbar vertebral fractures (OLVF) are crucial. Magnetic resonance imaging (MRI) is generally performed to differentiate between fresh and old OLVF. However, MRIs can be intolerable for patients with severe back pain. Furthermore, it is difficult to perform in an emergency. MRI should therefore only be performed in appropriately selected patients with a high suspicion of fresh fractures. As radiography is the first-choice imaging examination for the diagnosis of OLVF, improving screening accuracy with radiographs will optimize the decision of whether an MRI is necessary. This study aimed to develop a method to automatically classify lumbar vertebrae (LV) conditions such as normal, old, or fresh OLVF using deep learning methods with radiography. A total of 3481 LV images for training, validation, and testing and 662 LV images for external validation were collected. Visual evaluation by two radiologists determined the ground truth of LV diagnoses. Three convolutional neural networks were ensembled. The accuracy, sensitivity, and specificity were 0.89, 0.83, and 0.92 in the test and 0.84, 0.76, and 0.89 in the external validation, respectively. The results suggest that the proposed method can contribute to the accurate automatic classification of LV conditions on radiography. Full article

(This article belongs to the Special Issue Deep Learning and Data Analytics Techniques for Processing of Biomedical Images)

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21 pages, 3655 KiB

Open AccessArticle

Human Hepatocellular Carcinoma Classification from H&E Stained Histopathology Images with 3D Convolutional Neural Networks and Focal Loss Function

by Umut Cinar, Rengul Cetin Atalay and Yasemin Yardimci Cetin

J. Imaging 2023, 9(2), 25; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging9020025 - 21 Jan 2023

Cited by 4 | Viewed by 1979

Abstract

This paper proposes a new Hepatocellular Carcinoma (HCC) classification method utilizing a hyperspectral imaging system (HSI) integrated with a light microscope. Using our custom imaging system, we have captured 270 bands of hyperspectral images of healthy and cancer tissue samples with HCC diagnosis from a liver microarray slide. Convolutional Neural Networks with 3D convolutions (3D-CNN) have been used to build an accurate classification model. With the help of 3D convolutions, spectral and spatial features within the hyperspectral cube are incorporated to train a strong classifier. Unlike 2D convolutions, 3D convolutions take the spectral dimension into account while automatically collecting distinctive features during the CNN training stage. As a result, we have avoided manual feature engineering on hyperspectral data and proposed a compact method for HSI medical applications. Moreover, the focal loss function, utilized as a CNN cost function, enables our model to tackle the class imbalance problem residing in the dataset effectively. The focal loss function emphasizes the hard examples to learn and prevents overfitting due to the lack of inter-class balancing. Our empirical results demonstrate the superiority of hyperspectral data over RGB data for liver cancer tissue classification. We have observed that increased spectral dimension results in higher classification accuracy. Both spectral and spatial features are essential in training an accurate learner for cancer tissue classification. Full article

(This article belongs to the Special Issue Deep Learning and Data Analytics Techniques for Processing of Biomedical Images)

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Review

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19 pages, 970 KiB

Open AccessReview

Prospective of Pancreatic Cancer Diagnosis Using Cardiac Sensing

by Mansunderbir Singh, Priyanka Anvekar, Bhavana Baraskar, Namratha Pallipamu, Srikanth Gadam, Akhila Sai Sree Cherukuri, Devanshi N. Damani, Kanchan Kulkarni and Shivaram P. Arunachalam

J. Imaging 2023, 9(8), 149; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging9080149 - 25 Jul 2023

Viewed by 3404

Abstract

Pancreatic carcinoma (Ca Pancreas) is the third leading cause of cancer-related deaths in the world. The malignancies of the pancreas can be diagnosed with the help of various imaging modalities. An endoscopic ultrasound with a tissue biopsy is so far considered to be the gold standard in terms of the detection of Ca Pancreas, especially for lesions <2 mm. However, other methods, like computed tomography (CT), ultrasound, and magnetic resonance imaging (MRI), are also conventionally used. Moreover, newer techniques, like proteomics, radiomics, metabolomics, and artificial intelligence (AI), are slowly being introduced for diagnosing pancreatic cancer. Regardless, it is still a challenge to diagnose pancreatic carcinoma non-invasively at an early stage due to its delayed presentation. Similarly, this also makes it difficult to demonstrate an association between Ca Pancreas and other vital organs of the body, such as the heart. A number of studies have proven a correlation between the heart and pancreatic cancer. The tumor of the pancreas affects the heart at the physiological, as well as the molecular, level. An overexpression of the SMAD4 gene; a disruption in biomolecules, such as IGF, MAPK, and ApoE; and increased CA19-9 markers are a few of the many factors that are noted to affect cardiovascular systems with pancreatic malignancies. A comprehensive review of this correlation will aid researchers in conducting studies to help establish a definite relation between the two organs and discover ways to use it for the early detection of Ca Pancreas. Full article

(This article belongs to the Special Issue Deep Learning and Data Analytics Techniques for Processing of Biomedical Images)

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