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Computational Imaging, Sensing and Analysis for Biomedical Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 10514

Special Issue Editors

School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, China
Interests: medical signal/image processing; medical imaging; biomedical signal sensing; AI-based biomedical applications
National Heart and Lung Institute, Imperial College London, South Kensington, London SW7 2AZ, UK
Interests: medical image analysis; multimodal information fusion; data synthesis; data harmonisation
Special Issues, Collections and Topics in MDPI journals
School of Computer Science and Technology, Anhui University, Hefei 230601, China
Interests: computer vision; machine learning; medical image analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biomedical applications have become increasingly prevalent in recent years due to the leap in the computing ability of electronic devices and equipment. This poses a great challenge regarding how to present advanced biomedical computational models for imaging, sensing and analysis, especially those associated with artificial intelligence. The purpose of this Special Issue is to demonstrate the further exploration of methodologies and applications in biomedical computation, including: 1) proposing new solutions to address emerging challenges in biomedical applications; 2) tackling the inter-disciplinary usage of advanced computational methods (such as artificial intelligence) and new applications in the biomedical field.

This Special Issue will focus on (but will not be limited to) the following topics:

  1. Computer-aided medical imaging methods (CT, MRI, Ultrasound, PET imaging, etc.).
  2. Computer-aided medical sensing methods.
  3. Computer-aided medical analysis methods (medical image processing, medical signal processing , etc.).
  4. Evaluation methods for the biomedical computational model.
  5. Artificial-intelligence-based solutions in biomedical applications.
  6. New computational methods/applications in biology and physics for the medical field (gene analysis, material design and analysis, etc.).

Dr. Zhifan Gao
Dr. Guang Yang
Dr. Chenchu Xu
Guest Editors

Manuscript Submission Information

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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. Sensors 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.

Published Papers (4 papers)

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Research

19 pages, 7844 KiB  
Article
Spatial Smoothing Effect on Group-Level Functional Connectivity during Resting and Task-Based fMRI
by Cemre Candemir
Sensors 2023, 23(13), 5866; https://doi.org/10.3390/s23135866 - 24 Jun 2023
Cited by 1 | Viewed by 2657
Abstract
Spatial smoothing is a preprocessing step applied to neuroimaging data to enhance data quality by reducing noise and artifacts. However, selecting an appropriate smoothing kernel size can be challenging as it can lead to undesired alterations in final images and functional connectivity networks. [...] Read more.
Spatial smoothing is a preprocessing step applied to neuroimaging data to enhance data quality by reducing noise and artifacts. However, selecting an appropriate smoothing kernel size can be challenging as it can lead to undesired alterations in final images and functional connectivity networks. However, there is no sufficient information about the effects of the Gaussian kernel size on group-level results for different cases yet. This study investigates the influence of kernel size on functional connectivity networks and network parameters in whole-brain rs-fMRI and tb-fMRI analyses of healthy adults. The analysis includes {0, 2, 4, 6, 8, 10} mm kernels, commonly used in practical analyses, covering all major brain networks. Graph theoretical measures such as betweenness centrality, global/local efficiency, clustering coefficient, and average path length are examined for each kernel. Additionally, principal component analysis (PCA) and independent component analysis (ICA) parameters, namely kurtosis and skewness, are evaluated for the functional images. The findings demonstrate that kernel size directly affects node connections, resulting in modifications to functional network structures and PCA/ICA parameters. However, network metrics exhibit greater resilience to these changes. Full article
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19 pages, 3532 KiB  
Article
Impact of Anatomical Variability on Sensitivity Profile in fNIRS–MRI Integration
by Augusto Bonilauri, Francesca Sangiuliano Intra, Francesca Baglio and Giuseppe Baselli
Sensors 2023, 23(4), 2089; https://doi.org/10.3390/s23042089 - 13 Feb 2023
Cited by 2 | Viewed by 1735
Abstract
Functional near-infrared spectroscopy (fNIRS) is an important non-invasive technique used to monitor cortical activity. However, a varying sensitivity of surface channels vs. cortical structures may suggest integrating the fNIRS with the subject-specific anatomy (SSA) obtained from routine MRI. Actual processing tools permit the [...] Read more.
Functional near-infrared spectroscopy (fNIRS) is an important non-invasive technique used to monitor cortical activity. However, a varying sensitivity of surface channels vs. cortical structures may suggest integrating the fNIRS with the subject-specific anatomy (SSA) obtained from routine MRI. Actual processing tools permit the computation of the SSA forward problem (i.e., cortex to channel sensitivity) and next, a regularized solution of the inverse problem to map the fNIRS signals onto the cortex. The focus of this study is on the analysis of the forward problem to quantify the effect of inter-subject variability. Thirteen young adults (six males, seven females, age 29.3 ± 4.3) underwent both an MRI scan and a motor grasping task with a continuous wave fNIRS system of 102 measurement channels with optodes placed according to a 10/5 system. The fNIRS sensitivity profile was estimated using Monte Carlo simulations on each SSA and on three major atlases (i.e., Colin27, ICBM152 and FSAverage) for comparison. In each SSA, the average sensitivity curves were obtained by aligning the 102 channels and segmenting them by depth quartiles. The first quartile (depth < 11.8 (0.7) mm, median (IQR)) covered 0.391 (0.087)% of the total sensitivity profile, while the second one (depth < 13.6 (0.7) mm) covered 0.292 (0.009)%, hence indicating that about 70% of the signal was from the gyri. The sensitivity bell-shape was broad in the source–detector direction (20.953 (5.379) mm FWHM, first depth quartile) and steeper in the transversal one (6.082 (2.086) mm). The sensitivity of channels vs. different cortical areas based on SSA were analyzed finding high dispersions among subjects and large differences with atlas-based evaluations. Moreover, the inverse cortical mapping for the grasping task showed differences between SSA and atlas based solutions. In conclusion, integration with MRI SSA can significantly improve fNIRS interpretation. Full article
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14 pages, 2462 KiB  
Article
Lightweight Compound Scaling Network for Nasopharyngeal Carcinoma Segmentation from MR Images
by Yi Liu, Guanghui Han and Xiujian Liu
Sensors 2022, 22(15), 5875; https://doi.org/10.3390/s22155875 - 5 Aug 2022
Cited by 7 | Viewed by 1903
Abstract
Nasopharyngeal carcinoma (NPC) is a category of tumours with a high incidence in head-and-neck. To treat nasopharyngeal cancer, doctors invariably need to perform focal segmentation. However, manual segmentation is time consuming and laborious for doctors and the existing automatic segmentation methods require large [...] Read more.
Nasopharyngeal carcinoma (NPC) is a category of tumours with a high incidence in head-and-neck. To treat nasopharyngeal cancer, doctors invariably need to perform focal segmentation. However, manual segmentation is time consuming and laborious for doctors and the existing automatic segmentation methods require large computing resources, which makes some small and medium-sized hospitals unaffordable. To enable small and medium-sized hospitals with limited computational resources to run the model smoothly and improve the accuracy of structure, we propose a new LW-UNet network. The network utilises lightweight modules to form the Compound Scaling Encoder and combines the benefits of UNet to make the model both lightweight and accurate. Our model achieves a high accuracy with a Dice coefficient value of 0.813 with 3.55 M parameters and 7.51 G of FLOPs within 0.1 s (testing time in GPU), which is the best result compared with four other state-of-the-art models. Full article
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15 pages, 7173 KiB  
Article
CAFS: An Attention-Based Co-Segmentation Semi-Supervised Method for Nasopharyngeal Carcinoma Segmentation
by Yitong Chen, Guanghui Han, Tianyu Lin and Xiujian Liu
Sensors 2022, 22(13), 5053; https://doi.org/10.3390/s22135053 - 5 Jul 2022
Cited by 1 | Viewed by 2275
Abstract
Accurate segmentation of nasopharyngeal carcinoma is essential to its treatment effect. However, there are several challenges in existing deep learning-based segmentation methods. First, the acquisition of labeled data are challenging. Second, the nasopharyngeal carcinoma is similar to the surrounding tissues. Third, the shape [...] Read more.
Accurate segmentation of nasopharyngeal carcinoma is essential to its treatment effect. However, there are several challenges in existing deep learning-based segmentation methods. First, the acquisition of labeled data are challenging. Second, the nasopharyngeal carcinoma is similar to the surrounding tissues. Third, the shape of nasopharyngeal carcinoma is complex. These challenges make the segmentation of nasopharyngeal carcinoma difficult. This paper proposes a novel semi-supervised method named CAFS for automatic segmentation of nasopharyngeal carcinoma. CAFS addresses the above challenges through three mechanisms: the teacher–student cooperative segmentation mechanism, the attention mechanism, and the feedback mechanism. CAFS can use only a small amount of labeled nasopharyngeal carcinoma data to segment the cancer region accurately. The average DSC value of CAFS is 0.8723 on the nasopharyngeal carcinoma segmentation task. Moreover, CAFS has outperformed the state-of-the-art nasopharyngeal carcinoma segmentation methods in the comparison experiment. Among the compared state-of-the-art methods, CAFS achieved the highest values of DSC, Jaccard, and precision. In particular, the DSC value of CAFS is 7.42% higher than the highest DSC value in the state-of-the-art methods. Full article
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