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J. Imaging
Special Issues
Advancements in Imaging Techniques for Detection of Cancer
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Advancements in Imaging Techniques for Detection of Cancer

A special issue of Journal of Imaging (ISSN 2313-433X).

Deadline for manuscript submissions: 31 December 2024 | Viewed by 3219

Special Issue Editors

Dr. Bofan Song

E-Mail Website
Guest Editor
Wyant College of Optical Sciences, The University of Arizona, Tucson, AZ 85721, USA
Interests: biomedical optical imaging; biomedical optical instrumentation; point-of-care testing; early cancer detection; artificial intelligence for biomedical applications; reliable and interpretable AI
Special Issues, Collections and Topics in MDPI journals
Dr. Eung-Joo Lee

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Arizona, Tucson, AZ 85721, USA
Interests: computer vision; medical image analysis; artificial intelligence

Special Issue Information

Dear Colleagues,

Advancements in imaging techniques are crucial for the detection and treatment of cancer, offering non-invasive and high-resolution methods to understand the complexities of cancerous tissues. These techniques have a wide range of applications in oncology. This Special Issue will highlight the latest innovations in imaging technologies and their applications in cancer detection and treatment. The authors will showcase advancements in imaging techniques that are at the forefront of state-of-the-art technologies or provide novel or original solutions to existing clinical challenges in oncology. We welcome contributions (original research articles or reviews) from diverse fields that focus on the methods, implementation, and medical applications of imaging technologies in cancer detection. For this Special Issue, we encourage the integration of imaging techniques with other cutting-edge technologies, such as artificial intelligence, three-dimensional printing, materials and biochemistry etc., to enhance diagnostic precision and efficiency in oncology.

Dr. Bofan Song
Dr. Eung-Joo Lee
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

  • cancer detection
  • cancer imaging
  • non-invasive diagnosis
  • high-resolution imaging
  • early cancer detection
  • point-of-care detection
  • artificial intelligence in cancer imaging
  • optical coherence tomograph
  • confocal microscopy
  • fluorescence imaging
  • multispectral imaging
  • multiphoton imaging
  • biomarker imaging

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

18 pages, 12381 KiB  
Article
AQSA—Algorithm for Automatic Quantification of Spheres Derived from Cancer Cells in Microfluidic Devices
by Ana Belén Peñaherrera-Pazmiño, Ramiro Fernando Isa-Jara, Elsa Hincapié-Arias, Silvia Gómez, Denise Belgorosky, Eduardo Imanol Agüero, Matías Tellado, Ana María Eiján, Betiana Lerner and Maximiliano Pérez
J. Imaging 2024, 10(11), 295; https://doi.org/10.3390/jimaging10110295 - 20 Nov 2024
Viewed by 1004
Abstract
Sphere formation assay is an accepted cancer stem cell (CSC) enrichment method. CSCs play a crucial role in chemoresistance and cancer recurrence. Therefore, CSC growth is studied in plates and microdevices to develop prediction chemotherapy assays in cancer. As counting spheres cultured in [...] Read more.
Sphere formation assay is an accepted cancer stem cell (CSC) enrichment method. CSCs play a crucial role in chemoresistance and cancer recurrence. Therefore, CSC growth is studied in plates and microdevices to develop prediction chemotherapy assays in cancer. As counting spheres cultured in devices is laborious, time-consuming, and operator-dependent, a computational program called the Automatic Quantification of Spheres Algorithm (ASQA) that detects, identifies, counts, and measures spheres automatically was developed. The algorithm and manual counts were compared, and there was no statistically significant difference (p = 0.167). The performance of the AQSA is better when the input image has a uniform background, whereas, with a nonuniform background, artifacts can be interpreted as spheres according to image characteristics. The areas of spheres derived from LN229 cells and CSCs from primary cultures were measured. For images with one sphere, area measurements obtained with the AQSA and SpheroidJ were compared, and there was no statistically significant difference between them (p = 0.173). Notably, the AQSA detects more than one sphere, compared to other approaches available in the literature, and computes the sphere area automatically, which enables the observation of treatment response in the sphere derived from the human glioblastoma LN229 cell line. In addition, the algorithm identifies spheres with numbers to identify each one over time. The AQSA analyzes many images in 0.3 s per image with a low computational cost, enabling laboratories from developing countries to perform sphere counts and area measurements without needing a powerful computer. Consequently, it can be a useful tool for automated CSC quantification from cancer cell lines, and it can be adjusted to quantify CSCs from primary culture cells. CSC-derived sphere detection is highly relevant as it avoids expensive treatments and unnecessary toxicity. Full article
(This article belongs to the Special Issue Advancements in Imaging Techniques for Detection of Cancer)
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24 pages, 3240 KiB  
Article
ESFPNet: Efficient Stage-Wise Feature Pyramid on Mix Transformer for Deep Learning-Based Cancer Analysis in Endoscopic Video
by Qi Chang, Danish Ahmad, Jennifer Toth, Rebecca Bascom and William E. Higgins
J. Imaging 2024, 10(8), 191; https://doi.org/10.3390/jimaging10080191 - 7 Aug 2024
Viewed by 1667
Abstract
For patients at risk of developing either lung cancer or colorectal cancer, the identification of suspect lesions in endoscopic video is an important procedure. The physician performs an endoscopic exam by navigating an endoscope through the organ of interest, be it the lungs [...] Read more.
For patients at risk of developing either lung cancer or colorectal cancer, the identification of suspect lesions in endoscopic video is an important procedure. The physician performs an endoscopic exam by navigating an endoscope through the organ of interest, be it the lungs or intestinal tract, and performs a visual inspection of the endoscopic video stream to identify lesions. Unfortunately, this entails a tedious, error-prone search over a lengthy video sequence. We propose a deep learning architecture that enables the real-time detection and segmentation of lesion regions from endoscopic video, with our experiments focused on autofluorescence bronchoscopy (AFB) for the lungs and colonoscopy for the intestinal tract. Our architecture, dubbed ESFPNet, draws on a pretrained Mix Transformer (MiT) encoder and a decoder structure that incorporates a new Efficient Stage-Wise Feature Pyramid (ESFP) to promote accurate lesion segmentation. In comparison to existing deep learning models, the ESFPNet model gave superior lesion segmentation performance for an AFB dataset. It also produced superior segmentation results for three widely used public colonoscopy databases and nearly the best results for two other public colonoscopy databases. In addition, the lightweight ESFPNet architecture requires fewer model parameters and less computation than other competing models, enabling the real-time analysis of input video frames. Overall, these studies point to the combined superior analysis performance and architectural efficiency of the ESFPNet for endoscopic video analysis. Lastly, additional experiments with the public colonoscopy databases demonstrate the learning ability and generalizability of ESFPNet, implying that the model could be effective for region segmentation in other domains. Full article
(This article belongs to the Special Issue Advancements in Imaging Techniques for Detection of Cancer)
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