MeMoS — A software tool for extraction of anatomical structures data from 3D medical images

2016 ◽  
Author(s):  
Zbigniew Tyfa ◽  
Michal Strzelecki
Keyword(s):  
Medical Images ◽  
Software Tool ◽  
Anatomical Structures

scholarly journals Nextmed: Automatic Imaging Segmentation, 3D Reconstruction, and 3D Model Visualization Platform Using Augmented and Virtual Reality

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2962 ◽  
Author(s):  
Santiago González Izard ◽  
Ramiro Sánchez Torres ◽  
Óscar Alonso Plaza ◽  
Juan Antonio Juanes Méndez ◽  
Francisco José García-Peñalvo
Keyword(s):  
Virtual Reality ◽  
3D Reconstruction ◽  
Medical Image ◽  
New Technologies ◽  
Medical Images ◽  
3D Visualization ◽  
Automatic Segmentation ◽  
Individual Case ◽  
Medical Professionals ◽  
Anatomical Structures

The visualization of medical images with advanced techniques, such as augmented reality and virtual reality, represent a breakthrough for medical professionals. In contrast to more traditional visualization tools lacking 3D capabilities, these systems use the three available dimensions. To visualize medical images in 3D, the anatomical areas of interest must be segmented. Currently, manual segmentation, which is the most commonly used technique, and semi-automatic approaches can be time consuming because a doctor is required, making segmentation for each individual case unfeasible. Using new technologies, such as computer vision and artificial intelligence for segmentation algorithms and augmented and virtual reality for visualization techniques implementation, we designed a complete platform to solve this problem and allow medical professionals to work more frequently with anatomical 3D models obtained from medical imaging. As a result, the Nextmed project, due to the different implemented software applications, permits the importation of digital imaging and communication on medicine (dicom) images on a secure cloud platform and the automatic segmentation of certain anatomical structures with new algorithms that improve upon the current research results. A 3D mesh of the segmented structure is then automatically generated that can be printed in 3D or visualized using both augmented and virtual reality, with the designed software systems. The Nextmed project is unique, as it covers the whole process from uploading dicom images to automatic segmentation, 3D reconstruction, 3D visualization, and manipulation using augmented and virtual reality. There are many researches about application of augmented and virtual reality for medical image 3D visualization; however, they are not automated platforms. Although some other anatomical structures can be studied, we focused on one case: a lung study. Analyzing the application of the platform to more than 1000 dicom images and studying the results with medical specialists, we concluded that the installation of this system in hospitals would provide a considerable improvement as a tool for medical image visualization.

scholarly journals ConvNet-Based Localization of Anatomical Structures in 3-D Medical Images

2017 ◽  
Vol 36 (7) ◽  
pp. 1470-1481 ◽  
Author(s):  
Bob D. de Vos ◽  
Jelmer M. Wolterink ◽  
Pim A. de Jong ◽  
Tim Leiner ◽  
Max A. Viergever ◽  
...  
Keyword(s):  
Medical Images ◽  
Anatomical Structures

scholarly journals Exploiting Global Structure Information to Improve Medical Image Segmentation

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3249
Author(s):  
Jaemoon Hwang ◽  
Sangheum Hwang
Keyword(s):  
Medical Image ◽  
Medical Images ◽  
Feature Space ◽  
Medical Image Segmentation ◽  
Global Structure ◽  
Distance Metrics ◽  
Anatomical Structures ◽  
Structure Information ◽  
Anatomical Priors ◽  
Segmentation Models

In this paper, we propose a method to enhance the performance of segmentation models for medical images. The method is based on convolutional neural networks that learn the global structure information, which corresponds to anatomical structures in medical images. Specifically, the proposed method is designed to learn the global boundary structures via an autoencoder and constrain a segmentation network through a loss function. In this manner, the segmentation model performs the prediction in the learned anatomical feature space. Unlike previous studies that considered anatomical priors by using a pre-trained autoencoder to train segmentation networks, we propose a single-stage approach in which the segmentation network and autoencoder are jointly learned. To verify the effectiveness of the proposed method, the segmentation performance is evaluated in terms of both the overlap and distance metrics on the lung area and spinal cord segmentation tasks. The experimental results demonstrate that the proposed method can enhance not only the segmentation performance but also the robustness against domain shifts.

Toward Estimating the Uncertainty Associated With Three-Dimensional Geometry Reconstructed From Medical Image Data

2019 ◽  
Vol 4 (4) ◽  
Author(s):  
Marc Horner ◽  
Stephen M. Luke ◽  
Kerim O. Genc ◽  
Todd M. Pietila ◽  
Ross T. Cotton ◽  
...  
Keyword(s):  
Image Analysis ◽  
Computer Aided Design ◽  
Medical Images ◽  
Specific Treatment ◽  
Patient Specific ◽  
Similar Degree ◽  
Sphere Diameter ◽  
Reconstruction Algorithms ◽  
Anatomical Structures ◽  
Anatomical Data

Abstract Patient-specific computational modeling is increasingly used to assist with visualization, planning, and execution of medical treatments. This trend is placing more reliance on medical imaging to provide accurate representations of anatomical structures. Digital image analysis is used to extract anatomical data for use in clinical assessment/planning. However, the presence of image artifacts, whether due to interactions between the physical object and the scanning modality or the scanning process, can degrade image accuracy. The process of extracting anatomical structures from medical images introduces additional sources of variability, e.g., when thresholding or when eroding along apparent edges of biological structures. An estimate of the uncertainty associated with extracting anatomical data from medical images would therefore assist with assessing the reliability of patient-specific treatment plans. To this end, two image datasets were developed and analyzed using standard image analysis procedures. The first dataset was developed by performing a “virtual voxelization” of a computer-aided design (CAD) model of a sphere, representing the idealized scenario of no error in the image acquisition and reconstruction algorithms (i.e., a perfect scan). The second dataset was acquired by scanning three spherical balls using a laboratory-grade computed tomography (CT) scanner. For the idealized sphere, the error in sphere diameter was less than or equal to 2% if five or more voxels were present across the diameter. The measurement error degraded to approximately 4% for a similar degree of voxelization of the physical phantom. The adaptation of established thresholding procedures to improve segmentation accuracy was also investigated.

scholarly journals Visual Illusions in Radiology: Untrue Perceptions in Medical Images and Their Implications for Diagnostic Accuracy

2021 ◽  
Vol 15 ◽  
Author(s):  
Robert G. Alexander ◽  
Fahd Yazdanie ◽  
Stephen Waite ◽  
Zeshan A. Chaudhry ◽  
Srinivas Kolla ◽  
...  
Keyword(s):  
Diagnostic Accuracy ◽  
Medical Error ◽  
Medical Images ◽  
Visual Illusions ◽  
Computer Aided Detection ◽  
Patient Harm ◽  
Anatomical Structures ◽  
Computer Aided ◽  
Cause Of Error ◽  
Detection And Diagnosis

Errors in radiologic interpretation are largely the result of failures of perception. This remains true despite the increasing use of computer-aided detection and diagnosis. We surveyed the literature on visual illusions during the viewing of radiologic images. Misperception of anatomical structures is a potential cause of error that can lead to patient harm if disease is seen when none is present. However, visual illusions can also help enhance the ability of radiologists to detect and characterize abnormalities. Indeed, radiologists have learned to exploit certain perceptual biases in diagnostic findings and as training tools. We propose that further detailed study of radiologic illusions would help clarify the mechanisms underlying radiologic performance and provide additional heuristics to improve radiologist training and reduce medical error.

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