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Diagnostics
Special Issues
Artificial Intelligence and Robotics in Interventional Radiology
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Artificial Intelligence and Robotics in Interventional Radiology

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 30 April 2024 | Viewed by 2785

Special Issue Editor

Dr. Roberto Iezzi

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Guest Editor
Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia – Istituto di Radiologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, l.go A gemelli 8, 00168 Rome, Italy
Interests: artificial intelligence; robotics; big data; radiomic; interventional radiology; locoregional treatment; machine learning; deep learning

Special Issue Information

Dear Colleagues,

The development of artificial intelligence (AI) is largely based on the introduction of artificial neural networks (ANN) that allowed the introduction of the concepts of “computational learning models”, machine learning (ML), and deep learning (DL).

In recent years, AI in interventional radiology has quickly grown into a hot topic in medicine, and it appears to be poised to fundamentally transform and help to advance the field of diagnostic radiology.

This Special Issue aims to collect recent updates on machine learning, radiomics, and AI in the field of interventional radiology, enumerating the possible applications of such techniques, to ensure patients benefit the most.

Dr. Roberto Iezzi
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. Diagnostics 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 (2 papers)

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Research

16 pages, 5188 KiB  
Article
An Automatic Needle Puncture Path-Planning Method for Thermal Ablation of Lung Tumors
by Zhengshuai Wang, Weiwei Wu, Shuicai Wu, Zhuhuang Zhou and Honghai Zhang
Diagnostics 2024, 14(2), 215; https://doi.org/10.3390/diagnostics14020215 - 19 Jan 2024
Cited by 1 | Viewed by 660
Abstract
Computed tomography (CT)-guided thermal ablation is an emerging treatment method for lung tumors. Ablation needle path planning in preoperative diagnosis is of critical importance. In this work, we proposed an automatic needle path-planning method for thermal lung tumor ablation. First, based on the [...] Read more.
Computed tomography (CT)-guided thermal ablation is an emerging treatment method for lung tumors. Ablation needle path planning in preoperative diagnosis is of critical importance. In this work, we proposed an automatic needle path-planning method for thermal lung tumor ablation. First, based on the improved cube mapping algorithm, binary classification was performed on the surface of the bounding box of the patient’s CT image to obtain a feasible puncture area that satisfied all hard constraints. Then, for different clinical soft constraint conditions, corresponding grayscale constraint maps were generated, respectively, and the multi-objective optimization problem was solved by combining Pareto optimization and weighted product algorithms. Finally, several optimal puncture paths were planned within the feasible puncture area obtained for the clinicians to choose. The proposed method was evaluated with 18 tumors of varying sizes (482.79 mm3 to 9313.81 mm3) and the automatically planned paths were compared and evaluated with manually planned puncture paths by two clinicians. The results showed that over 82% of the paths (74 of 90) were considered reasonable, with clinician A finding the automated planning path superior in 7 of 18 cases, and clinician B in 9 cases. Additionally, the time efficiency of the algorithm (35 s) was much higher than that of manual planning. The proposed method is expected to aid clinicians in preoperative path planning for thermal ablation of lung tumors. By providing a valuable reference for the puncture path during preoperative diagnosis, it may reduce the clinicians’ workload and enhance the objectivity and rationality of the planning process, which in turn improves the effectiveness of treatment. Full article
(This article belongs to the Special Issue Artificial Intelligence and Robotics in Interventional Radiology)
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25 pages, 13542 KiB  
Article
Comparative Analysis of Image Processing Techniques for Enhanced MRI Image Quality: 3D Reconstruction and Segmentation Using 3D U-Net Architecture
by Chee Chin Lim, Apple Ho Wei Ling, Yen Fook Chong, Mohd Yusoff Mashor, Khalilalrahman Alshantti and Mohd Ezane Aziz
Diagnostics 2023, 13(14), 2377; https://doi.org/10.3390/diagnostics13142377 - 14 Jul 2023
Cited by 1 | Viewed by 1770
Abstract
Osteosarcoma is a common type of bone tumor, particularly prevalent in children and adolescents between the ages of 5 and 25 who are experiencing growth spurts during puberty. Manual delineation of tumor regions in MRI images can be laborious and time-consuming, and results [...] Read more.
Osteosarcoma is a common type of bone tumor, particularly prevalent in children and adolescents between the ages of 5 and 25 who are experiencing growth spurts during puberty. Manual delineation of tumor regions in MRI images can be laborious and time-consuming, and results may be subjective and difficult to replicate. Therefore, a convolutional neural network (CNN) was developed to automatically segment osteosarcoma cancerous cells in three types of MRI images. The study consisted of five main stages. First, 3692 DICOM format MRI images were acquired from 46 patients, including T1-weighted, T2-weighted, and T1-weighted with injection of Gadolinium (T1W + Gd) images. Contrast stretching and median filter were applied to enhance image intensity and remove noise, and the pre-processed images were reconstructed into NIfTI format files for deep learning. The MRI images were then transformed to fit the CNN’s requirements. A 3D U-Net architecture was proposed with optimized parameters to build an automatic segmentation model capable of segmenting osteosarcoma from the MRI images. The 3D U-Net segmentation model achieved excellent results, with mean dice similarity coefficients (DSC) of 83.75%, 85.45%, and 87.62% for T1W, T2W, and T1W + Gd images, respectively. However, the study found that the proposed method had some limitations, including poorly defined borders, missing lesion portions, and other confounding factors. In summary, an automatic segmentation method based on a CNN has been developed to address the challenge of manually segmenting osteosarcoma cancerous cells in MRI images. While the proposed method showed promise, the study revealed limitations that need to be addressed to improve its efficacy. Full article
(This article belongs to the Special Issue Artificial Intelligence and Robotics in Interventional Radiology)
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