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Mathematics
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AI for Hyperspectral and Medical Imaging
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AI for Hyperspectral and Medical Imaging

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 7013

Special Issue Editors

Prof. Dr. Radu Tudor Ionescu

E-Mail Website
Guest Editor
Department of Computer Science, University of Bucharest, 14 Academiei, 010014 Bucharest, Romania
Interests: artificial intelligence; machine learning; computer vision; image processing; text mining; computational linguistics; medical imaging
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ion Necoara

E-Mail Website
Guest Editor
Faculty of Automation and Computers, University Politehnica of Bucharest, 313 Sp. Indenpendentei, 060042 Bucharest, Romania
Interests: optimization; gradient methods; convex programming; matrix algebra; artificial intelligence; hyperspectral imaging

Special Issue Information

Dear Colleagues,

With the ever-increasing amounts of data around us, many modern technologies designed for hyperspectral image processing and medical imagining now routinely use Artificial Intelligence. Hyperspectral is used in a wide array of applications, such as agriculture, health, environment, mineralogy, surveillance, physics, astronomy, and chemical imaging. Although there are many methods of tackling image processing problems, most of them are designed for application to color and/or grayscale images, and have limited success when applied to hyperspectral images. This is partially due to large hyperspectral datasets being difficult to collect, and the heavy computational load associated with images captured using many spectral bands.

This Special Issue aims to push the frontiers of AI and take important steps towards making the hyperspectral technologies of tomorrow possible, with direct impact on various parts of the environment, health, and industry sectors. Our goal is to gather novel processing, learning, and optimization techniques for hyperspectral imaging systems under a single umbrella.

We welcome articles on efficient and effective processing, learning, and optimization algorithms for hyperspectral imaging models with applications in ocean monitoring and medical images. Articles that present novel datasets or discuss challenges and open problems related to hyperspectral and medical image processing will also be considered. In summation, the topics of interest are:

  • Hyperspectral/medical imaging;
  • Hyperspectral/medical image registration;
  • Hyperspectral/medical image super-resolution;
  • Hyperspectral/medical image segmentation;
  • Representation learning for hyperspectral/medical imaging.

Prof. Dr. Radu Ionescu
Prof. Dr. Ion Necoara
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. Mathematics is an international peer-reviewed open access semimonthly 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

  • hyperspectral imaging
  • medical imaging
  • artificial intelligence
  • image registration
  • image super-resolution
  • image segmentation
  • representation learning
  • detection and classification

Published Papers (4 papers)

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Research

20 pages, 5164 KiB  
Article
A Lightweight Deep Learning Approach for Liver Segmentation
by Smaranda Bogoi and Andreea Udrea
Mathematics 2023, 11(1), 95; https://0-doi-org.brum.beds.ac.uk/10.3390/math11010095 - 26 Dec 2022
Cited by 5 | Viewed by 1964
Abstract
Liver segmentation is a prerequisite for various hepatic interventions and is a time-consuming manual task performed by radiology experts. Recently, various computationally expensive deep learning architectures tackled this aspect without considering the resource limitations of a real-life clinical setup. In this paper, we [...] Read more.
Liver segmentation is a prerequisite for various hepatic interventions and is a time-consuming manual task performed by radiology experts. Recently, various computationally expensive deep learning architectures tackled this aspect without considering the resource limitations of a real-life clinical setup. In this paper, we investigated the capabilities of a lightweight model, UNeXt, in comparison with the U-Net model. Moreover, we conduct a broad analysis at the micro and macro levels of these architectures by using two training loss functions: soft dice loss and unified focal loss, and by substituting the commonly used ReLU activation function, with the novel Funnel activation function. An automatic post-processing step that increases the overall performance of the models is also proposed. Model training and evaluation were performed on a public database—LiTS. The results show that the UNeXt model (Funnel activation, soft dice loss, post-processing step) achieved a 0.9902 dice similarity coefficient on the whole CT volumes in the test set, with 15× fewer parameters in nearly 4× less inference time, compared to its counterpart, U-Net. Thus, lightweight models can become the new standard in medical segmentation, and when implemented thoroughly can alleviate the computational burden while preserving the capabilities of a parameter-heavy architecture. Full article
(This article belongs to the Special Issue AI for Hyperspectral and Medical Imaging)
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25 pages, 24376 KiB  
Article
Application of Image Processing and 3D Printing Technique to Development of Computer Tomography System for Automatic Segmentation and Quantitative Analysis of Pulmonary Bronchus
by Chung Feng Jeffrey Kuo, Zheng-Xun Yang, Wen-Sen Lai and Shao-Cheng Liu
Mathematics 2022, 10(18), 3354; https://0-doi-org.brum.beds.ac.uk/10.3390/math10183354 - 15 Sep 2022
Cited by 1 | Viewed by 1519
Abstract
This study deals with the development of a computer tomography (CT) system for automatic segmentation and quantitative analysis of the pulmonary bronchus. It includes three parts. Part I employed an adaptive median and four neighbors low pass filters to eliminate the noise of [...] Read more.
This study deals with the development of a computer tomography (CT) system for automatic segmentation and quantitative analysis of the pulmonary bronchus. It includes three parts. Part I employed an adaptive median and four neighbors low pass filters to eliminate the noise of CT. Then, k-means clustering was used to segment the lung region in the CT data. In Part II, the pulmonary airway was segmented. The three-grade segmentation was employed to divide all pixels in the lung region into three uncertain grades, including air, blood vessels, and tissues, and uncertain portions. The airway wall was reformed using a border pixel weight mask. Afterwards, the seed was calculated automatically with the front-end image masking the aggregation position of the lung region as the input of the region growing to obtain the initial airway. Afterwards, the micro bronchi with different radii were detected using morphological grayscale reconstruction to modify the initial airway. Part III adopted skeletonization to simplify the pulmonary airway, keeping the length and extension direction information. The information was recorded in a linked list with the world coordinates based on the patients’ carina, defined by the directions of the carina to the top end of the trachea and right and left main bronchi. The whole set of bronchi was recognized by matching the target bronchus direction and world coordinates using hierarchical classification. The proposed system could detect the location of the pulmonary airway and detect 11 generations’ bronchi with a bronchus recognition capability of 98.33%. Meanwhile, 20 airway parameters’ measurement and 3D printing verification have been processed. The diameter, length, volume, angle, and cross-sectional area of the main trachea and the right and left bronchi, the cross-sectional area of the junction, the left bronchus length, and the right bronchus length have been calculated for clinical practice guidelines. The system proposed in this study simultaneously maintained the advantages of automation and high accuracy and contributed to clinical diagnosis. Full article
(This article belongs to the Special Issue AI for Hyperspectral and Medical Imaging)
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14 pages, 969 KiB  
Article
Spatiality Sensitive Learning for Cancer Metastasis Detection in Whole-Slide Images
by Haixia Zheng, Yu Zhou and Xin Huang
Mathematics 2022, 10(15), 2657; https://0-doi-org.brum.beds.ac.uk/10.3390/math10152657 - 28 Jul 2022
Cited by 2 | Viewed by 1154
Abstract
Metastasis detection in lymph nodes via microscopic examination of histopathological images is one of the most crucial diagnostic procedures for breast cancer staging. The manual analysis is extremely labor-intensive and time-consuming because of complexities and diversities of histopathology images. Deep learning has been [...] Read more.
Metastasis detection in lymph nodes via microscopic examination of histopathological images is one of the most crucial diagnostic procedures for breast cancer staging. The manual analysis is extremely labor-intensive and time-consuming because of complexities and diversities of histopathology images. Deep learning has been utilized in automatic cancer metastasis detection in recent years. Due to the huge size of whole-slide images, most existing approaches split each image into smaller patches and simply treat these patches independently, which ignores the spatial correlations among them. To solve this problem, this paper proposes an effective spatially sensitive learning framework for cancer metastasis detection in whole-slide images. Moreover, a novel spatial loss function is designed to ensure the consistency of prediction over neighboring patches. Specifically, through incorporating long short-term memory and spatial loss constraint on top of a convolutional neural network feature extractor, the proposed method can effectively learn both the appearance of each patch and spatial relationships between adjacent image patches. With the standard back-propagation algorithm, the whole framework can be trained in an end-to-end way. Finally, the regions with high tumor probability in the resulting probability map are the metastasis locations. Extensive experiments on the benchmark Camelyon 2016 Grand Challenge dataset show the effectiveness of the proposed approach with respect to state-of-the-art competitors. The obtained precision, recall, and balanced accuracy are 0.9565, 0.9167, and 0.9458, respectively. It is also demonstrated that the proposed approach can provide more accurate detection results and is helpful for early diagnosis of cancer metastasis. Full article
(This article belongs to the Special Issue AI for Hyperspectral and Medical Imaging)
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13 pages, 882 KiB  
Article
Improving Cancer Metastasis Detection via Effective Contrastive Learning
by Haixia Zheng, Yu Zhou and Xin Huang
Mathematics 2022, 10(14), 2404; https://0-doi-org.brum.beds.ac.uk/10.3390/math10142404 - 08 Jul 2022
Cited by 3 | Viewed by 1642
Abstract
The metastasis detection in lymph nodes via microscopic examination of H&E stained histopathological images is one of the most crucial diagnostic procedures for breast cancer staging. The manual analysis is extremely labor-intensive and time-consuming because of complexities and diversities of histopathological images. Deep [...] Read more.
The metastasis detection in lymph nodes via microscopic examination of H&E stained histopathological images is one of the most crucial diagnostic procedures for breast cancer staging. The manual analysis is extremely labor-intensive and time-consuming because of complexities and diversities of histopathological images. Deep learning has been utilized in automatic cancer metastasis detection in recent years. The success of supervised deep learning is credited to a large labeled dataset, which is hard to obtain in medical image analysis. Contrastive learning, a branch of self-supervised learning, can help in this aspect through introducing an advanced strategy to learn discriminative feature representations from unlabeled images. In this paper, we propose to improve breast cancer metastasis detection through self-supervised contrastive learning, which is used as an accessional task in the detection pipeline, allowing a feature extractor to learn more valuable representations, even if there are fewer annotation images. Furthermore, we extend the proposed approach to exploit unlabeled images in a semi-supervised manner, as self-supervision does not need labeled data at all. Extensive experiments on the benchmark Camelyon2016 Grand Challenge dataset demonstrate that self-supervision can improve cancer metastasis detection performance leading to state-of-the-art results. Full article
(This article belongs to the Special Issue AI for Hyperspectral and Medical Imaging)
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