Research

20 pages, 2731 KiB

Open AccessArticle

3D Ultrasonic Brain Imaging with Deep Learning Based on Fully Convolutional Networks

by Jiahao Ren, Xiaocen Wang, Chang Liu, He Sun, Junkai Tong, Min Lin, Jian Li, Lin Liang, Feng Yin, Mengying Xie and Yang Liu

Sensors 2023, 23(19), 8341; https://doi.org/10.3390/s23198341 - 09 Oct 2023

Viewed by 1288

Abstract

Compared to magnetic resonance imaging (MRI) and X-ray computed tomography (CT), ultrasound imaging is safer, faster, and more widely applicable. However, the use of conventional ultrasound in transcranial brain imaging for adults is predominantly hindered by the high acoustic impedance contrast between the [...] Read more.

Compared to magnetic resonance imaging (MRI) and X-ray computed tomography (CT), ultrasound imaging is safer, faster, and more widely applicable. However, the use of conventional ultrasound in transcranial brain imaging for adults is predominantly hindered by the high acoustic impedance contrast between the skull and soft tissue. This study introduces a 3D AI algorithm, Brain Imaging Full Convolution Network (BIFCN), combining waveform modeling and deep learning for precise brain ultrasound reconstruction. We constructed a network comprising one input layer, four convolution layers, and one pooling layer to train our algorithm. In the simulation experiment, the Pearson correlation coefficient between the reconstructed and true images was exceptionally high. In the laboratory, the results showed a slightly lower but still impressive coincidence degree for 3D reconstruction, with pure water serving as the initial model and no prior information required. The 3D network can be trained in 8 h, and 10 samples can be reconstructed in just 12.67 s. The proposed 3D BIFCN algorithm provides a highly accurate and efficient solution for mapping wavefield frequency domain data to 3D brain models, enabling fast and precise brain tissue imaging. Moreover, the frequency shift phenomenon of blood may become a hallmark of BIFCN learning, offering valuable quantitative information for whole-brain blood imaging. Full article

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

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24 pages, 6800 KiB

Open AccessArticle

Automatic Multiview Alignment of RGB-D Range Maps of Upper Limb Anatomy

by Luca Di Angelo, Paolo Di Stefano, Emanuele Guardiani, Paolo Neri, Alessandro Paoli and Armando Viviano Razionale

Sensors 2023, 23(18), 7841; https://doi.org/10.3390/s23187841 - 12 Sep 2023

Viewed by 827

Abstract

Digital representations of anatomical parts are crucial for various biomedical applications. This paper presents an automatic alignment procedure for creating accurate 3D models of upper limb anatomy using a low-cost handheld 3D scanner. The goal is to overcome the challenges associated with forearm [...] Read more.

Digital representations of anatomical parts are crucial for various biomedical applications. This paper presents an automatic alignment procedure for creating accurate 3D models of upper limb anatomy using a low-cost handheld 3D scanner. The goal is to overcome the challenges associated with forearm 3D scanning, such as needing multiple views, stability requirements, and optical undercuts. While bulky and expensive multi-camera systems have been used in previous research, this study explores the feasibility of using multiple consumer RGB-D sensors for scanning human anatomies. The proposed scanner comprises three Intel^® RealSenseTM D415 depth cameras assembled on a lightweight circular jig, enabling simultaneous acquisition from three viewpoints. To achieve automatic alignment, the paper introduces a procedure that extracts common key points between acquisitions deriving from different scanner poses. Relevant hand key points are detected using a neural network, which works on the RGB images captured by the depth cameras. A set of forearm key points is meanwhile identified by processing the acquired data through a specifically developed algorithm that seeks the forearm’s skeleton line. The alignment process involves automatic, rough 3D alignment and fine registration using an iterative-closest-point (ICP) algorithm expressly developed for this application. The proposed method was tested on forearm scans and compared the results obtained by a manual coarse alignment followed by an ICP algorithm for fine registration using commercial software. Deviations below 5 mm, with a mean value of 1.5 mm, were found. The obtained results are critically discussed and compared with the available implementations of published methods. The results demonstrate significant improvements to the state of the art and the potential of the proposed approach to accelerate the acquisition process and automatically register point clouds from different scanner poses without the intervention of skilled operators. This study contributes to developing effective upper limb rehabilitation frameworks and personalized biomedical applications by addressing these critical challenges. Full article

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

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25 pages, 16089 KiB

Open AccessArticle

Fast Three-Dimensional Posture Reconstruction of Motorcyclists Using OpenPose and a Custom MATLAB Script

by Emmanuele Barberi, Massimiliano Chillemi, Filippo Cucinotta and Felice Sfravara

Sensors 2023, 23(17), 7415; https://doi.org/10.3390/s23177415 - 25 Aug 2023

Cited by 1 | Viewed by 1203

Abstract

Ergonomics focuses on the analysis of the interaction between human beings and their working environment. During the riding of a motorbike, ergonomics studies the rider’s posture on the motorbike. An incorrect posture can lead to physical and psychological discomfort, and can affect the [...] Read more.

Ergonomics focuses on the analysis of the interaction between human beings and their working environment. During the riding of a motorbike, ergonomics studies the rider’s posture on the motorbike. An incorrect posture can lead to physical and psychological discomfort, and can affect the perception of risk and the handling of the motorcycle. It is important for motorcyclists to adopt a good riding posture, for their health and road safety. The aim of this work is to propose a fast, cheap, and sufficiently robust method for the 3D reconstruction of the posture assumed by a motorcyclist. The stereo vision and the application of OpenPose made it possible to obtain a 3D reconstruction of the key points, and their evolution over time. The evaluation of the distances between the 3D key points, which represent the length of the various parts of the body, appears to remain sufficiently stable over time, and faithful to the real distances, as taken on the motorcyclist themself. The 3D reconstruction obtained can be applied in different fields: ergonomics, motorsport training, dynamics, and fluid dynamics analysis. Full article

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

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23 pages, 12888 KiB

Open AccessArticle

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

by Damir Filko and Emmanuel Karlo Nyarko

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

Cited by 2 | Viewed by 1927

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 [...] Read more.

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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12 pages, 8034 KiB

Open AccessArticle

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

by Jiayi Huo, Changjiang Zhou, Bo Yuan, Qing Yang and Liqiang Wang

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

Cited by 3 | Viewed by 2206

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 [...] Read more.

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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21 pages, 7136 KiB

Open AccessArticle

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

by 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 10 | Viewed by 3418

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 [...] Read more.

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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16 pages, 4302 KiB

Open AccessArticle

Inertial Tracking System for Monitoring Dual Mobility Hip Implants In Vitro

by Matthew Peter Shuttleworth, Oliver Vickers, Mackenzie Smeeton, Tim Board, Graham Isaac, Peter Culmer, Sophie Williams and Robert William Kay

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

Cited by 1 | Viewed by 1571

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 [...] Read more.

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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14 pages, 3195 KiB

Open AccessArticle

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

by Andrej Thurzo, Martin Strunga, Romana Havlínová, Katarína Reháková, Renata Urban, Jana Surovková and Veronika Kurilová

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

Cited by 19 | Viewed by 3696

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 [...] Read more.

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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16 pages, 3698 KiB

Open AccessArticle

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

by Marco Mandolini, Agnese Brunzini, Giulia Facco, Alida Mazzoli, Archimede Forcellese and Antonio Gigante

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

Cited by 17 | Viewed by 2769

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 [...] Read more.

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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