The Corpus Callosum is the major interhemispheric commisure and, because of its highly organized fibers, it is often studied using diffusion tensor images (DTI). A firstnecessary step for CC studies is its segmentation, preferably automated. Since most available softwares are not able to perform CC volumetric segmentation, and the only ones that do it, are based on T1-weighted images and not DTI, this work presents the extension of an open-source software, called inCCsight, incorporating a DTI-based CC volumetric segmentation method into it. The software is open-source and offers the possibility of incorporating customized plots and integrating other segmentation and/or parcellation methods by the user.
The Corpus callosum (CC) is a massive white matter structure in the brain, and changes in its shape and volume are associated with subject characteristics, several diseases, and clinical conditions. The CC is mostly studied in magnetic resonance imaging (MRI), where it is segmented to extract valuable information. With the increasing availability of MRI data and the proliferation of automated algorithms to perform CC segmentation, quality control (QC) verification is mandatory to assure reliability in the entire analysis pipeline. We propose a convolutional neural network (CNN) for QC of CC segmentations. The CNN gets information on the mask and contextual information on the image and performs deep feature extraction using a pre-trained model. The CNN model was fine-tuned using T1-weighted MR images with CC masks, in the task of classifying correct or incorrect segmentations. The CNN-based approach got an area under the curve (AUC) of 97.98% on the test set. We used an additional test set of patients with tumor to test generalization capability of the trained model to other domains.
The Coronavirus Disease 2019 (COVID-19) pandemic that affects the world since 2020 generated a great amount of research interest in how to provide aid to medical staff on triage, diagnosis, and prognosis. This work proposes an automated segmentation model over Computed Tomography (CT) scans, segmenting the lung and COVID-19 related lung findings at the same time. Manual segmentation is a time-consuming and complex task, especially when applied to high-resolution CT scans, resulting in a lack of gold standards annotation. Thanks to data provided by the RadVid19 Brazilian initiative, providing over a hundred annotated High Resolution CT (HRCT), we analyze the performance of three convolutional neural networks for the segmentation of lung and COVID findings: a 3D UNet architecture; a modified EfficientDet (2D) architecture; and 3D and 2D variations of the MobileNetV3 architecture. Our method achieved first place in the RadVid19 challenge, among 13 other competitors’ submissions. Additionally, we evaluate the model with the best result on the challenge in four public CT datasets, comparing our results against other related works, and studying the effects of using different annotations in training and testing. Our best method achieved on testing upwards of 0.98 Lung and 0.73 Findings 3D Dice and reached state-of-the-art performance on public data.
Corpus callosum (CC) segmentation is an important first step of MRI-based analysis, however most available automated methods and tools perform its segmentation on the midsagittal slice only. Additionally, the few volumetric CC segmentation methods available work on T1-weighted images, what requires an additional step of registering the T1 segmentation mask over diffusion tensor images (DTI) when conducting any DTI-based analysis. This work presents a volumetric segmentation method of the corpus callosum using a modified U-Net on diffusion tensor data, such as Fractional Anisotropy (FA), Mean Difusivity (MD) and Mode of Anisotropy (MO). The model was trained on 70 DTI acquisitions and tested on a dataset composed of 14 acquisitions with manual volumetric segmentation. Results indicate that using multiple DTI maps as input channels is better than using a single one. The best model obtained a mean dice of 83,29% on the test dataset, surpassing the performance of available softwares.
The thalamus is an internal structure of the brain whose changes are related to diseases such as multiple sclerosis and Parkinson’s disease. Thus, the thalamus segmentation is an important step in studies and applications related to these disorders, for example, for shape measuring and surgical planning. The most used software and tools for brain structures segmentation employ atlas-based algorithms that usually require long processing times and sometimes lead to inaccurate results on sub-cortical structures. New methods, that minimize those problems, using deep learning for segmenting brain structures have been recently proposed. However, for some structures such as the thalamus, these methods still tend to have unsatisfactory results since they rely only on T1w images, where the contrast can be low or absent. Aiming to overcome these issues, we proposed a Convolutional Neural Network (CNN) trained with multi-modal data (structural and diffusion MRI) and the use of silver standard masks created from multiple automatic segmentations. Results on a subset of 190 subjects from the Human Connectome Project (HCP) showed an improvement in segmentation quality, confirming the effectiveness of diffusion data in differentiating tissues due to measured micro-structural properties.
Magnetic resonance spectroscopic imaging (MRSI) has been widely used for studying metabolic alterations in brain-related pathologies, especially due to its non-invasiveness. Even though some software for MRSI data analysis have been developed, only a few are used by biomedical researchers and in a clinical setting routine, as their use still poses a challenge in keeping the trade-off between information content and ease of implementation. Aiming to increase MRSI analysis automation, our study proposes an open-source toolbox for analysis, automatic spectra quality control and MRSI data visualization. The proposed toolbox allows the visual inspection of all spectra. It makes possible the automated selection of spectra of interest using two different approaches: by clustering them using Pearson’s correlation coefficient or by discarding spectra based on spectral quality metrics. Using the magnetic resonance imaging (MRI) content, the toolbox provides information about the region from which the MRSI grid were acquired, such as the brain tissue ratio in each voxel: white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). Once spatial and spectral information is combined, spatial averaging over anatomically defined regions of interest (ROIs) can be applied, for instance, by averaging the spectra and fitting the result. The proposed toolbox aims to simplify and automate MRSI analysis, being easy to install and to use.
Current techniques trying to predict Alzheimer's disease at an early-stage explore the structural information of T1-weighted MR Images. Among these techniques, deep convolutional neural network (CNN) is the most promising since it has been successfully used in a variety of medical imaging problems. However, the majority of works on Alzheimer's Disease tackle the binary classification problem only, i.e., to distinguish Normal Controls from Alzheimer's Disease patients. Only a few works deal with the multiclass problem, namely, patient classification into one of the three groups: Normal Control (NC), Alzheimer's Disease (AD) or Mild Cognitive Impairment (MCI). In this paper, our primary goal is to tackle the 3-class AD classification problem using T1-weighted MRI and a 2D CNN approach. We used the first two layers of ResNet34 as feature extractor and then trained a classifier using 64 × 64 sized patches from coronal 2D MRI slices. Our extended-2D CNN proposal explores the MRI volumetric information, by using non-consecutive 2D slices as input channels of the CNN, while maintaining the low computational costs associated with a 2D approach. The proposed model, trained and tested on images from ADNI dataset, achieved an accuracy of 68.6% for the multiclass problem, presenting the best performance when compared to state-of-the-art AD classification methods, even the 3D-CNN based ones.
Hypothalamus is a small structure of the brain with an important role in sleep, appetite, body temperature regulation and emotion. Some neurological diseases, such as Schizophrenia, Alzheimer and Amyotrophic Lateral Sclerosis (ALS) may be related to hypothalamic volume variation. However, hypothalamic morphological landmarks are not always clear on magnetic resonance (MR) images and manual segmentation can become variable, leading to inconsistent findings in the literature. In this work, we propose a fully automatic segmentation method, with no human interaction, to segment hypothalamus in MR images using convolutional neural networks (CNNs). The best performance was obtained by a consensus model using the majority voting from three 2D-CNNs trained on axial, coronal and sagittal MRI slices, achieving a DICE coefficient of 0.77. To the best of our knowledge, this is the first work to fully automatically segment the hypothalamus.
Magnetic resonance spectroscopy (MRS) has been widely used for studying metabolic changes in rheumatic, neurodegenerative diseases and several other types of pathologies. Nevertheless, the accurate measurement of brain metabolite concentrations is still problematic and challenging, specially for multivoxel MR Spectroscopic Imaging (MRSI) data. There is a collection of artifacts and spectra are acquired from a region containing mixed tissues: white matter (WM), grey matter (GM) and cerebrospinal uid (CSF) composition. However, the studies are interested in analyzing metabolite changes in a particular brain tissue or structure. Therefore, our work proposes a pipeline for automatic selection of spectra of interest, a subset of spectra from MRSI acquisitions based on MRI content analysis and spectral quality metrics. The proposed pipeline helps to improve multivoxel spectroscopy analysis and estimates of metabolite concentrations, by eliminating spectra outside the tissue or structure of interest and identifying noisy spectra.
Corpus Callosum (CC) is the largest white matter structure and it plays a crucial role in clinical and research studies due to its shape and volume correlation to subject’s characteristics and neurodegenerative diseases. CC segmentation and parcellation are an important step for any MRI-based clinical and research study. There is only a few automatic CC parcellation methods proposed in the literature and, since it is not trivial to build a ground truth, most methods are validated qualitatively. We present a quantitative analysis of different state of art CC parcellation methods aiming to compare their results on a common dataset. Our findings show a significant difference among the same CC parcels, but using different CC parcellation methods, and its impact on the diffusion properties.
The Corpus Callosum (CC) is the largest white matter structure in the brain and subject of many relevant studies. In order to properly analyze this structure in 2D studies, the midsagittal plane (MSP) determination of the CC is required. Usually, this computation is done on structural MR images and transformed to diffusion space when necessary. Furthermore, most existing methods take into account the whole brain structure instead of only the object of study. Differently, our work proposes a plane computation based on the structure of interest, directly on Diffusion Tensor Images (DTI), through the DTI-based divergence map.1 Since our plane is computed in the diffusion domain, the method explores the high organization of the fibers in the CC to establish a reference system that can be used to perform 2D CC studies, while most existing MSP computation algorithms are based on structural characteristics of the brain, such as shape symmetry and inter-hemispheric fissure location. Experiments showed that the proposed method is reliable regarding repeatability and parameters choices. Results also indicate that the callosal fiber convergence plane (CFCP) found by our method is similar to MSP in most subjects. Nevertheless, when the CC is not well aligned with the brain intercommissural fissure, CFCP and MSP presented significant differences.
Lesions in the brain white matter are among the most frequently observed incidental findings on MR images. This paper presents a 3D texture-based classification to distinguish normal appearing white matter from white matter containing lesions, and compares it with the 2D approach. Texture analysis were based on 55 texture attributes extracted from gray-level histogram, gray-level co-occurrence matrix, run-length matrix and gradient. The results show that the 3D approach achieves an accuracy rate of 99.28%, against 97.41% of the 2D approach by using a support vector machine classifier. Furthermore, the most discriminating texture attributes on both 2D and 3D cases were obtained from the image histogram and co-occurrence matrix.
Medical imaging research depends basically on the availability of large image collections, image processing and analysis algorithms, hardware and a multidisciplinary research team. It has to be reproducible, free of errors, fast, accessible through a large variety of devices spread around research centers and conducted simultaneously by a multidisciplinary team. Therefore, we propose a collaborative research environment, named Adessowiki, where tools and datasets are integrated and readily available in the Internet through a web browser. Moreover, processing history and all intermediate results are stored and displayed in automatic generated web pages for each object in the research project or clinical study. It requires no installation or configuration from the client side and offers centralized tools and specialized hardware resources, since processing takes place in the cloud.
The corpus callosum is the major brain structure responsible for inter{hemispheric communication between neurons. Many studies seek to relate corpus callosum attributes to patient characteristics, cerebral diseases and psychological disorders. Most of those studies rely on 2D analysis of the corpus callosum in the mid-sagittal plane. However, it is common to find conflicting results among studies, once many ignore methodological issues and define the mid-sagittal plane based on precary or invalid criteria with respect to the corpus callosum. In this work we propose a novel method to determine the mid-callosal plane using the corpus callosum internal preferred diffusion directions obtained from diffusion tensor images. This plane is analogous to the mid-sagittal plane, but intended to serve exclusively as the corpus callosum reference. Our method elucidates the great potential the directional information of the corpus callosum fibers have to indicate its own referential. Results from experiments with five image pairs from distinct subjects, obtained under the same conditions, demonstrate the method effectiveness to find the corpus callosum symmetric axis relative to the axial plane.
Brain white matter lesions found upon magnetic resonance imaging are often observed in psychiatric or neurological patients. Individuals with these lesions present a more significant cognitive impairment when compared with individuals without them. We propose a computerized method to distinguish tissue containing white matter lesions of different etiologies (e.g., demyelinating or ischemic) using texture-based classifiers. Texture attributes were extracted from manually selected regions of interest and used to train and test supervised classifiers. Experiments were conducted to evaluate texture attribute discrimination and classifiers’ performances. The most discriminating texture attributes were obtained from the gray-level histogram and from the co-occurrence matrix. The best classifier was the support vector machine, which achieved an accuracy of 87.9% in distinguishing lesions with different etiologies and an accuracy of 99.29% in distinguishing normal white matter from white matter lesions.
Computerized automatic anatomy recognition (AAR) is an essential step for implementing body-wide quantitative radiology (QR). Our strategy to automatically identify and delineate various organs in a given body region is based on fuzzy models and an organ hierarchy. In previous years, the basic algorithms of our AAR approach - model building, recognition, and delineation - and their evaluation were presented. In the present paper, we propose to replace the single fuzzy model built for each organ by a set of fuzzy models built for the same organ. Based on a dataset composed of CT images of the Thorax region of 50 subjects, our experiments indicate that recognition performance improves when using multiple models instead of a single model for each organ. It is interesting to point out that the improvement is not uniform for all organs, leading us to conclude that some organs will benefit from the multiple model approach more than others.
The brain white matter is responsible for the transmission of electrical signals through the central nervous system.
Lesions in the brain white matter, called white matter hyperintensity (WMH), can cause a significant functional deficit.
WMH are commonly seen in normal aging, but also in a number of neurological and psychiatric disorders. We propose
here an automatic method for WHM analysis in order to distinguish regions of interest between normal and non-normal
white matter (identification task) and also to distinguish different types of lesions based on their etiology: demyelinating
or ischemic (classification task). The method combines texture analysis with the use of classifiers, such as Support
Vector Machine (SVM), Nearst Neighboor (1NN), Linear Discriminant Analysis (LDA) and Optimum Path Forest
(OPF). Experiments with real brain MRI data showed that the proposed method is suitable to identify and classify the
brain lesions.
The corpus callosum (CC) is one of the most important white matter structures of the brain, interconnecting the two
cerebral hemispheres, and is related to several neurodegenerative diseases. Since segmentation is usually the first step for
studies in this structure, and manual volumetric segmentation is a very time-consuming task, it is important to have a
robust automatic method for CC segmentation. We propose here an approach for fully automatic 3D segmentation of the
CC in the magnetic resonance diffusion tensor images. The method uses the watershed transform and is performed on the
fractional anisotropy (FA) map weighted by the projection of the principal eigenvector in the left-right direction. The
section of the CC in the midsagittal slice is used as seed for the volumetric segmentation. Experiments with real diffusion
MRI data showed that the proposed method is able to quickly segment the CC without any user intervention, with great
results when compared to manual segmentation. Since it is simple, fast and does not require parameter settings, the
proposed method is well suited for clinical applications.
This paper presents a segmentation technique for diffusion tensor imaging (DTI). This technique is based on a
tensorial morphological gradient (TMG), defined as the maximum dissimilarity over the neighborhood. Once
this gradient is computed, the tensorial segmentation problem becomes an scalar one, which can be solved
by conventional techniques, such as watershed transform and thresholding. Similarity functions, namely the
dot product, the tensorial dot product, the J-divergence and the Frobenius norm, were compared, in order to
understand their differences regarding the measurement of tensor dissimilarities. The study showed that the dot
product and the tensorial dot product turned out to be inappropriate for computation of the TMG, while the
Frobenius norm and the J-divergence were both capable of measuring tensor dissimilarities, despite the distortion
of Frobenius norm, since it is not an affine invariant measure. In order to validate the TMG as a solution for DTI
segmentation, its computation was performed using distinct similarity measures and structuring elements. TMG
results were also compared to fractional anisotropy. Finally, synthetic and real DTI were used in the method
validation. Experiments showed that the TMG enables the segmentation of DTI by watershed transform or by a
simple choice of a threshold. The strength of the proposed segmentation method is its simplicity and robustness,
consequences of TMG computation. It enables the use, not only of well-known algorithms and tools from the
mathematical morphology, but also of any other segmentation method to segment DTI, since TMG computation
transforms tensorial images in scalar ones.
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