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Special Issue "3D Sensing and Imaging for Biomedical Investigations"

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

Deadline for manuscript submissions: 25 April 2023 | Viewed by 4909

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Special Issue Editors

Prof. Dr. Anna Eva Morabito

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Guest Editor

Department of Engineering Innovation, University of Salento, 73100 Lecce, Italy
Interests: feature recognition; reverse engineering; 3D acquisition; virtual prototyping; computational geometry; CAD modeling; shape analysis
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Prof. Dr. Luca Di Angelo

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Guest Editor

Department of Industrial and Information Engineering and Economics, University of L’Aquila, 67040 L’Aquila, Italy
Interests: 3D acquisition; computational geometry; feature recognition; reverse engineering; virtual prototyping; shape analysis; 3D shape recognition and classification
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Dr. Emanuele Guardiani

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Co-Guest Editor

Department of Industrial and Information Engineering and Economics, University of L’Aquila, L'Aquila, Italy
Interests: discrete geometries; 3D scanning; geometric segmentation; augmented reality
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Special Issue Information

Dear Colleagues,

In recent years, there has been a considerable development of sensors for three-dimensional acquisition in the biomedical field with the aim of both reducing the electromagnetic radiation to which the patient is subjected and improving their resolution and accuracy. This is opening up new scenarios for diagnostics, treatment planning, and follow-up procedures and studies. Obviously, each sensor providing information with different accuracy and resolution requires specific algorithms for quantitative data extraction. Regardless, the need to process raw data still represents a limitation in exploiting the potential of three-dimensional digitization of parts of the human body. To date, in fact, despite the availability of three-dimensional virtual models, in most cases, traditional manual protocols are used. The development of methods for automatic digital data processing is of uttermost importance, and various research groups are currently working on the subject. Moreover, the availability of these types of data could increase the potentialities of diagnosis and treatment planning tools, offering the possibility of analyzing quantities that cannot be investigated using traditional methods.

This Special Issue of Sensors, entitled “3D Sensing and Imaging for Bioengineering Investigations”, will focus on all innovative aspects of research and development related to this field. Original research papers focusing on the development and experimental verification of computer-based sensing and imaging algorithms for biomedical fields are welcome.

We invite authors to contribute reviews and original research articles that will illustrate and stimulate ongoing research in this field of biomedical engineering. Reviews should provide an up-to-date overview of the current state of the art in a particular application and include key findings from different research groups.

Prof. Dr. Anna Morabito
Prof. Dr. Luca Di Angelo
Dr. Emanuele Guardiani
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. 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 2400 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

Three-dimensional measurements for biomedical investigations
Bio-measurements
Pattern recognition for biomedical investigations
Bio-imaging
Bio-sensing
Biomedical computer-aided design
Biomedical computer-aided applications

Published Papers (6 papers)

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Research

Open AccessArticle

2D/3D Wound Segmentation and Measurement Based on a Robot-Driven Reconstruction System

Damir Filko

and

Emmanuel Karlo Nyarko

Sensors 2023, 23(6), 3298; https://doi.org/10.3390/s23063298 - 21 Mar 2023

Viewed by 182

Abstract

Chronic wounds, are a worldwide health problem affecting populations and economies as a whole. With the increase in age-related diseases, obesity, and diabetes, the costs of chronic wound healing will further increase. Wound assessment should be fast and accurate in order to reduce possible complications and thus shorten the wound healing process. This paper describes an automatic wound segmentation based on a wound recording system built upon a 7-DoF robot arm with an attached RGB-D camera and high-precision 3D scanner. The developed system represents a novel combination of 2D and 3D segmentation, where the 2D segmentation is based on the MobileNetV2 classifier and the 3D component is based on the active contour model, which works on the 3D mesh to further refine the wound contour. The end output is the 3D model of only the wound surface without the surrounding healthy skin and geometric parameters in the form of perimeter, area, and volume. Full article

(This article belongs to the Special Issue 3D Sensing and Imaging for Biomedical Investigations)

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Open AccessArticle

Real-Time Dense Reconstruction with Binocular Endoscopy Based on StereoNet and ORB-SLAM

and

Sensors 2023, 23(4), 2074; https://doi.org/10.3390/s23042074 - 12 Feb 2023

Viewed by 462

Abstract

Binocular endoscopy is gradually becoming the future of minimally invasive surgery (MIS) thanks to the development of stereo vision. However, some problems still exist, such as the low reconstruction accuracy, small surgical field, and low computational efficiency. To solve these problems, we designed a framework for real-time dense reconstruction in binocular endoscopy scenes. First, we obtained the initial disparity map using an SGBM algorithm and proposed the disparity confidence map as a dataset to provide StereoNet training. Then, based on the depth map predicted by StereoNet, the corresponding left image of each depth map was input into the Oriented Fast and Brief-Simultaneous Localization and Mapping (ORB-SLAM) framework using an RGB-D camera to realize the real-time dense reconstruction of the binocular endoscopy scene. The proposed algorithm was verified in the stomach phantom and a real pig stomach. Compared with the ground truth, the proposed algorithm’s RMSE is 1.620 mm, and the number of effective points in the point cloud is 834,650, which is a significant improvement in the mapping ability compared with binocular SLAM and ensures the real-time performance of the algorithm while performing dense reconstruction. The effectiveness of the proposed algorithm is verified. Full article

(This article belongs to the Special Issue 3D Sensing and Imaging for Biomedical Investigations)

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Open AccessArticle

3D Multi-Modality Medical Imaging: Combining Anatomical and Infrared Thermal Images for 3D Reconstruction

Mauren Abreu de Souza

Daoana Carolaine Alka Cordeiro

Jonathan de Oliveira

Mateus Ferro Antunes de Oliveira

and

Beatriz Leandro Bonafini

Sensors 2023, 23(3), 1610; https://doi.org/10.3390/s23031610 - 01 Feb 2023

Cited by 1 | Viewed by 562

Abstract

Medical thermography provides an overview of the human body with two-dimensional (2D) information that assists the identification of temperature changes, based on the analysis of surface distribution. However, this approach lacks spatial depth information, which can be enhanced by adding multiple images or three-dimensional (3D) systems. Therefore, the methodology applied for this paper generates a 3D point cloud (from thermal infrared images), a 3D geometry model (from CT images), and the segmented inner anatomical structures. Thus, the following computational processing was employed: Structure from Motion (SfM), image registration, and alignment (affine transformation) between the 3D models obtained to combine and unify them. This paper presents the 3D reconstruction and visualization of the respective geometry of the neck/bust and inner anatomical structures (thyroid, trachea, veins, and arteries). Additionally, it shows the whole 3D thermal geometry in different anatomical sections (i.e., coronal, sagittal, and axial), allowing it to be further examined by a medical team, improving pathological assessments. The generation of 3D thermal anatomy models allows for a combined visualization, i.e., functional and anatomical images of the neck region, achieving encouraging results. These 3D models bring correlation of the inner and outer regions, which could improve biomedical applications and future diagnosis with such a methodology. Full article

(This article belongs to the Special Issue 3D Sensing and Imaging for Biomedical Investigations)

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Open AccessArticle

Inertial Tracking System for Monitoring Dual Mobility Hip Implants In Vitro

Matthew Peter Shuttleworth

and

Sensors 2023, 23(2), 904; https://doi.org/10.3390/s23020904 - 12 Jan 2023

Viewed by 526

Abstract

Dual mobility (DM) implants are being increasingly used for total hip arthroplasties due to the additional range of motion and joint stability they afford over more traditional implant types. Currently, there are no reported methods for monitoring their motions under realistic operating conditions while in vitro and, therefore, it is challenging to predict how they will function under clinically relevant conditions and what failure modes may exist. This study reports the development, calibration, and validation of a novel inertial tracking system that directly mounts to the mobile liner of DM implants. The tracker was custom built and based on a miniaturized, off-the-shelf inertial measurement unit (IMU) and employed a gradient-decent sensor fusion algorithm for amalgamating nine degree-of-freedom IMU readings into three-axis orientation estimates. Additionally, a novel approach to magnetic interference mitigation using a fixed solenoid and magnetic field simulation was evaluated. The system produced orientation measurements to within 1.0° of the true value under ideal conditions and 3.9° with a negligible drift while in vitro, submerged in lubricant, and without a line of sight. Full article

(This article belongs to the Special Issue 3D Sensing and Imaging for Biomedical Investigations)

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Open AccessArticle

Smartphone-Based Facial Scanning as a Viable Tool for Facially Driven Orthodontics?

and

Sensors 2022, 22(20), 7752; https://doi.org/10.3390/s22207752 - 12 Oct 2022

Cited by 3 | Viewed by 1275

Abstract

The current paradigm shift in orthodontic treatment planning is based on facially driven diagnostics. This requires an affordable, convenient, and non-invasive solution for face scanning. Therefore, utilization of smartphones’ TrueDepth sensors is very tempting. TrueDepth refers to front-facing cameras with a dot projector in Apple devices that provide real-time depth data in addition to visual information. There are several applications that tout themselves as accurate solutions for 3D scanning of the face in dentistry. Their clinical accuracy has been uncertain. This study focuses on evaluating the accuracy of the Bellus3D Dental Pro app, which uses Apple’s TrueDepth sensor. The app reconstructs a virtual, high-resolution version of the face, which is available for download as a 3D object. In this paper, sixty TrueDepth scans of the face were compared to sixty corresponding facial surfaces segmented from CBCT. Difference maps were created for each pair and evaluated in specific facial regions. The results confirmed statistically significant differences in some facial regions with amplitudes greater than 3 mm, suggesting that current technology has limited applicability for clinical use. The clinical utilization of facial scanning for orthodontic evaluation, which does not require accuracy in the lip region below 3 mm, can be considered. Full article

(This article belongs to the Special Issue 3D Sensing and Imaging for Biomedical Investigations)

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Open AccessArticle

Comparison of Three 3D Segmentation Software Tools for Hip Surgical Planning

and

Sensors 2022, 22(14), 5242; https://doi.org/10.3390/s22145242 - 13 Jul 2022

Cited by 3 | Viewed by 1082

Abstract

In hip arthroplasty, preoperative planning is fundamental to reaching a successful surgery. Nowadays, several software tools for computed tomography (CT) image processing are available. However, research studies comparing segmentation tools for hip surgery planning for patients affected by osteoarthritic diseases or osteoporotic fractures are still lacking. The present work compares three different software from the geometric, dimensional, and usability perspectives to identify the best three-dimensional (3D) modelling tool for the reconstruction of pathological femoral heads. Syngo.via Frontier (by Siemens Healthcare) is a medical image reading and post-processing software that allows low-skilled operators to produce prototypes. Materialise (by Mimics) is a commercial medical modelling software. 3D Slicer (by slicer.org) is an open-source development platform used in medical and biomedical fields. The 3D models reconstructed starting from the in vivo CT images of the pathological femoral head are compared with the geometries obtained from the laser scan of the in vitro bony specimens. The results show that Mimics and 3D Slicer are better for dimensional and geometric accuracy in the 3D reconstruction, while syngo.via Frontier is the easiest to use in the hospital setting. Full article

(This article belongs to the Special Issue 3D Sensing and Imaging for Biomedical Investigations)

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