Different Medical Image Registration Techniques: A Comparative Analysis

Convolutional Neural Network Techniques for Brain Tumor Classification (from 2015 to 2022): Review, Challenges, and Future Perspectives

Convolutional neural networks (CNNs) constitute a widely used deep learning approach that has frequently been applied to the problem of brain tumor diagnosis. Such techniques still face some critical challenges in moving towards clinic application. The main objective of this work is to present a comprehensive review of studies using CNN architectures to classify brain tumors using MR images with the aim of identifying useful strategies for and possible impediments in the development of this technology. Relevant articles were identified using a predefined, systematic procedure. For each article, data were extracted regarding training data, target problems, the network architecture, validation methods, and the reported quantitative performance criteria. The clinical relevance of the studies was then evaluated to identify limitations by considering the merits of convolutional neural networks and the remaining challenges that need to be solved to promote the clinical application and development of CNN algorithms. Finally, possible directions for future research are discussed for researchers in the biomedical and machine learning communities. A total of 83 studies were identified and reviewed. They differed in terms of the precise classification problem targeted and the strategies used to construct and train the chosen CNN. Consequently, the reported performance varied widely, with accuracies of 91.63–100% in differentiating meningiomas, gliomas, and pituitary tumors (26 articles) and of 60.0–99.46% in distinguishing low-grade from high-grade gliomas (13 articles). The review provides a survey of the state of the art in CNN-based deep learning methods for brain tumor classification. Many networks demonstrated good performance, and it is not evident that any specific methodological choice greatly outperforms the alternatives, especially given the inconsistencies in the reporting of validation methods, performance metrics, and training data encountered. Few studies have focused on clinical usability.

U-Net-Based Segmentation of Microscopic Images of Colorants and Simplification of Labeling in the Learning Process

Colored product textures correspond to particle size distributions. The microscopic images of colorants must be divided into regions to determine the particle size distribution. The conventional method used for this process involves manually dividing images into areas, which may be inefficient. In this paper, we have overcome this issue by developing two different modified architectures of U-Net convolution neural networks to automatically determine the particle sizes. To develop these modified architectures, a significant amount of ground truth data must be prepared to train the U-Net, which is difficult for big data as the labeling is performed manually. Therefore, we also aim to reduce this process by using incomplete labeling data. The first objective of this study is to determine the accuracy of our modified U-Net architectures for this type of image. The second objective is to reduce the difficulty of preparing the ground truth data by testing the accuracy of training on incomplete labeling data. The results indicate that efficient segmentation can be realized using our modified U-Net architectures, and the generation of ground truth data can be simplified. This paper presents a preliminary study to improve the efficiency of determining particle size distributions with incomplete labeling data.

Construction of 3D model of knee joint motion based on MRI image registration

There is a growing demand for information and computational technology for surgeons help with surgical planning as well as prosthetics design. The two-dimensional images are registered to the three-dimensional (3D) model for high efficiency. To reconstruct the 3D model of knee joint including bone structure and main soft tissue structure, the evaluation and analysis of sports injury and rehabilitation treatment are detailed in this study. Mimics 10.0 was used to reconstruct the bone structure, ligament, and meniscus according to the pulse diffusion-weighted imaging sequence (PDWI) and stir sequences of magnetic resonance imaging (MRI). Excluding congenital malformations and diseases of the skeletal muscle system, MRI scanning was performed on bilateral knee joints. Proton weighted sequence (PDWI sequence) and stir pulse sequence were selected for MRI. The models were imported into Geomagic Studio 11 software for refinement and modification, and 3D registration of bone structure and main soft tissue structure was performed to construct a digital model of knee joint bone structure and accessory cartilage and ligament structure. The 3D knee joint model including bone, meniscus, and collateral ligament was established. Reconstruction and image registration based on mimics and Geomagic Studio can build a 3D model of knee joint with satisfactory morphology, which can meet the requirements of teaching, motion simulation, and biomechanical analysis.

Yellow Pigment Powders Based on Lead and Antimony: Particle Size and Colour Hue

This paper reports the results of particle size analysis and colour measurements concerning yellow powders, synthesised in our laboratories according to ancient recipes aiming at producing pigments for paintings, ceramics, and glasses. These pigments are based on lead and antimony as chemical elements, that, combined in different proportions and fired at different temperatures, times, and with various additives, gave materials of yellow colours, changing in hues and particle size. Artificial yellow pigments, based on lead and antimony, have been widely studied, but no specific investigation on particle size distribution and its correlation to colour hue has been performed before. In order to evaluate the particle size distribution, segmentation of sample data has been performed using the MATLAB software environment. The extracted parameters were examined by principal component analysis (PCA) in order to detect differences and analogies between samples on the base of those parameters. Principal component analysis was also applied to colour data acquired by a reflectance spectrophotometer in the visible range according to the CIELAB colour space. Within the two examined groups, i.e., yellows containing NaCl and those containing K-tartrate, differences have been found between samples and also between different areas of the same powder indicating the inhomogeneity of the synthesised pigments. On the other hand, colour data showed homogeneity within each yellow sample and clear differences between the different powders. The comparison of results demonstrates the potentiality of the particle segmentation and analysis in the study of morphology and distribution of pigment powders produced artificially, allowing the characterisation of the lead and antimony-based pigments through micro-image analysis and colour measurements combined with a multivariate approach.

A novel 2D/3D hierarchical registration framework via principal-directional Fourier transform operator

An effective registration framework between preoperative 3D computed tomography and intraoperative 2D x-ray images is crucial in image-guided therapy. In this paper, a novel 2D/3D hierarchical registration framework via principal-directional Fourier transform operator (HRF-PDFTO) is proposed. First, a PDFTO was established to obtain the in-plane translation and rotation invariance. Then, an initial free template-matching approach based on PDFTO was utilized to avoid initial value assignment and expand the capture range of registration. Finally, the hierarchical registration framework, HRF-PDFTO, was proposed to reduce the dimensions of the registration search space from n ⁶ to n ². The experimental results demonstrated that the proposed HRF-PDFTO has good performance with an accuracy of 0.72 mm, and a single registration time of 16 s, which improves the registration efficiency by ten times. Consequently, the HRF-PDFTO can meet the accuracy and efficiency requirements of 2D/3D registration in related clinical applications.

Assessment of reference areas for superimposition of serial 3D models of patients with advanced periodontitis for volumetric soft tissue evaluation

AIM

This study aimed to determine the optimal reference area for superimposition of serial 3D dental models of patients with advanced periodontitis.

MATERIALS AND METHODS

Ten pre- and post-periodontal treatment 3D models (median time lapse: 13.1 months) of patients with advanced periodontitis were acquired by intraoral scanning. Superimposition was performed with the iterative closest point algorithm using four reference areas: (A) all stable teeth, (B) all teeth, (C) third palatal rugae and (D) the whole model. The superimposition accuracy was evaluated at two stable evaluation regions using the mean absolute distance and evaluated with two-way ANOVA and post-hoc multivariate model. The intra- and inter-operator reproducibility was calculated by intraclass correlation coefficient (ICC).

RESULTS

Superimposition accuracy evaluated at stable tooth evaluation region were 71 ± 29 μm, 73 ± 21 μm, 127 ± 52 μm and 113 ± 53 μm for areas A, B, C and D, respectively. All reference areas showed similarly high ICC values >0.990, except for reference area C showing ICC of 0.821 (intra-operator) and 0.767 (inter-operator) for tooth evaluation area.

CONCLUSIONS

Area A and B provide the highest accuracy for superimposition of serial 3D dental models acquired by intraoral scanning of patients with advanced periodontitis.