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Biomedical Image Processing and Sensing Application
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Biomedical Image Processing and Sensing Application

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

Deadline for manuscript submissions: closed (30 January 2023) | Viewed by 5637

Special Issue Editor

Dr. Tariq Mahmood Khan

E-Mail Website
Guest Editor
School of Computer Science and Engineering, UNSW, Sydney, NSW 2052, Australia
Interests: image enhancement; pattern recognition; image analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Monitoring technologies for human health have recently experienced a rapid and exciting expansion. New methods for detecting, monitoring, and tracing health-related biomarkers in clinical, pre-clinical, and home settings have been made possible by technological advances in hardware and software systems. This technological shift has resulted in enhanced healthcare efficacy, including earlier disease detection, novel treatment methods, and quantitative self-monitoring.

The recent increase in computational power and sensors has enabled the development of new sensing technologies as well as new analysis techniques that utilize well-established modalities. For instance, advancements in machine learning and image processing techniques have become pervasive across various aspects of biomedical imaging and sensing for the identification of intricate patterns. Similarly, hardware advancements have facilitated the development of health monitoring technologies in challenging environments, such as ambulatory monitoring. To bring together various aspects of health monitoring, authors are invited to submit papers describing novel imaging and/or sensing methods with pre-clinical/clinical applications to this Special Issue.  The core themes of this topic include, but are not limited to:

  • Advances in image analysis for disease detection and/or monitoring, including, but not limited to, MRI, X-ray, ultrasound, etc.
  • Machine learning methods for biomedical image and signal analysis.
  • Advances in multimodal systems for diagnosis, treatment, and/or prevention.

Dr. Tariq Mahmood Khan
Guest Editor

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. 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.

Keywords

  • medical image analysis
  • machine learning
  • biomedical imaging
  • convolutional neural networks
  • MRI
  • X-ray
  • imaging-based diagnostic

Published Papers (2 papers)

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Research

13 pages, 1454 KiB  
Article
Artificial Intelligence-Based Detection of Human Embryo Components for Assisted Reproduction by In Vitro Fertilization
by Abeer Mushtaq, Maria Mumtaz, Ali Raza, Nema Salem and Muhammad Naveed Yasir
Sensors 2022, 22(19), 7418; https://doi.org/10.3390/s22197418 - 29 Sep 2022
Cited by 4 | Viewed by 2832
Abstract
Assisted reproductive technology is helping humans by addressing infertility using different medical procedures that help in a successful pregnancy. In vitro fertilization (IVF) is one of those assisted reproduction methods in which the sperm and eggs are combined outside the human body in [...] Read more.
Assisted reproductive technology is helping humans by addressing infertility using different medical procedures that help in a successful pregnancy. In vitro fertilization (IVF) is one of those assisted reproduction methods in which the sperm and eggs are combined outside the human body in a specialized environment and kept for growth. Assisted reproductive technology is helping humans by addressing infertility using different medical procedures that help in a successful pregnancy. The morphology of the embryological components is highly related to the success of the assisted reproduction procedure. In approximately 3–5 days, the embryo transforms into the blastocyst. To prevent the multiple-birth risk and to increase the chance of pregnancy the embryologist manually analyzes the blastocyst components and selects valuable embryos to transfer to the women’s uterus. The manual microscopic analysis of blastocyst components, such as trophectoderm, zona pellucida, blastocoel, and inner cell mass, is time-consuming and requires keen expertise to select a viable embryo. Artificial intelligence is easing medical procedures by the successful implementation of deep learning algorithms that mimic the medical doctor’s knowledge to provide a better diagnostic procedure that helps in reducing the diagnostic burden. The deep learning-based automatic detection of these blastocyst components can help to analyze the morphological properties to select viable embryos. This research presents a deep learning-based embryo component segmentation network (ECS-Net) that accurately detects trophectoderm, zona pellucida, blastocoel, and inner cell mass for embryological analysis. The proposed method (ECS-Net) is based on a shallow deep segmentation network that uses two separate streams produced by a base convolutional block and a depth-wise separable convolutional block. Both streams are densely concatenated in combination with two dense skip paths to produce powerful features before and after upsampling. The proposed ECS-Net is evaluated on a publicly available microscopic blastocyst image dataset, the experimental segmentation results confirm the efficacy of the proposed method. The proposed ECS-Net is providing a mean Jaccard Index (Mean JI) of 85.93% for embryological analysis. Full article
(This article belongs to the Special Issue Biomedical Image Processing and Sensing Application)
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18 pages, 9579 KiB  
Article
Singular Nuclei Segmentation for Automatic HER2 Quantification Using CISH Whole Slide Images
by Md Shakhawat Hossain, M. M. Mahbubul Syeed, Kaniz Fatema, Md Sakir Hossain and Mohammad Faisal Uddin
Sensors 2022, 22(19), 7361; https://doi.org/10.3390/s22197361 - 28 Sep 2022
Cited by 6 | Viewed by 2166
Abstract
Human epidermal growth factor receptor 2 (HER2) quantification is performed routinely for all breast cancer patients to determine their suitability for HER2-targeted therapy. Fluorescence in situ hybridization (FISH) and chromogenic in situ hybridization (CISH) are the US Food and Drug [...] Read more.
Human epidermal growth factor receptor 2 (HER2) quantification is performed routinely for all breast cancer patients to determine their suitability for HER2-targeted therapy. Fluorescence in situ hybridization (FISH) and chromogenic in situ hybridization (CISH) are the US Food and Drug Administration (FDA) approved tests for HER2 quantification in which at least 20 cancer-affected singular nuclei are quantified for HER2 grading. CISH is more advantageous than FISH for cost, time and practical usability. In clinical practice, nuclei suitable for HER2 quantification are selected manually by pathologists which is time-consuming and laborious. Previously, a method was proposed for automatic HER2 quantification using a support vector machine (SVM) to detect suitable singular nuclei from CISH slides. However, the SVM-based method occasionally failed to detect singular nuclei resulting in inaccurate results. Therefore, it is necessary to develop a robust nuclei detection method for reliable automatic HER2 quantification. In this paper, we propose a robust U-net-based singular nuclei detection method with complementary color correction and deconvolution adapted for accurate HER2 grading using CISH whole slide images (WSIs). The efficacy of the proposed method was demonstrated for automatic HER2 quantification during a comparison with the SVM-based approach. Full article
(This article belongs to the Special Issue Biomedical Image Processing and Sensing Application)
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