scholarly journals Deep learning from MRI-derived labels enables automatic brain tissue classification on human brain CT

NeuroImage ◽  
2021 ◽  
pp. 118606
Author(s):  
Meera Srikrishna ◽  
Joana B. Pereira ◽  
Rolf A. Heckemann ◽  
Giovanni Volpe ◽  
Danielle van Westen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Human Brain ◽  
Brain Tissue ◽  
Tissue Classification ◽  
Brain Ct

scholarly journals Deep Learning-Based Framework for In Vivo Identification of Glioblastoma Tumor using Hyperspectral Images of Human Brain

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 920 ◽  
Author(s):  
Himar Fabelo ◽  
Martin Halicek ◽  
Samuel Ortega ◽  
Maysam Shahedi ◽  
Adam Szolna ◽  
...  
Keyword(s):  
Deep Learning ◽  
Human Brain ◽  
Brain Tissue ◽  
Tumor Resection ◽  
Hyperspectral Images ◽  
Support Vector ◽  
Label Free ◽  
Thematic Map ◽  
Operating Surgeon

The main goal of brain cancer surgery is to perform an accurate resection of the tumor, preserving as much normal brain tissue as possible for the patient. The development of a non-contact and label-free method to provide reliable support for tumor resection in real-time during neurosurgical procedures is a current clinical need. Hyperspectral imaging is a non-contact, non-ionizing, and label-free imaging modality that can assist surgeons during this challenging task without using any contrast agent. In this work, we present a deep learning-based framework for processing hyperspectral images of in vivo human brain tissue. The proposed framework was evaluated by our human image database, which includes 26 in vivo hyperspectral cubes from 16 different patients, among which 258,810 pixels were labeled. The proposed framework is able to generate a thematic map where the parenchymal area of the brain is delineated and the location of the tumor is identified, providing guidance to the operating surgeon for a successful and precise tumor resection. The deep learning pipeline achieves an overall accuracy of 80% for multiclass classification, improving the results obtained with traditional support vector machine (SVM)-based approaches. In addition, an aid visualization system is presented, where the final thematic map can be adjusted by the operating surgeon to find the optimal classification threshold for the current situation during the surgical procedure.

scholarly journals Comparison of Two-Dimensional- and Three-Dimensional-Based U-Net Architectures for Brain Tissue Classification in One-Dimensional Brain CT

2022 ◽  
Vol 15 ◽  
Author(s):  
Meera Srikrishna ◽  
Rolf A. Heckemann ◽  
Joana B. Pereira ◽  
Giovanni Volpe ◽  
Anna Zettergren ◽  
...  
Keyword(s):  
Deep Learning ◽  
Brain Tissue ◽  
Three Dimensional ◽  
Two Dimensional ◽  
3D Segmentation ◽  
Tissue Classification ◽  
Tissue Segmentation ◽  
One Dimensional ◽  
Ct Brain ◽  
2D And 3D

Brain tissue segmentation plays a crucial role in feature extraction, volumetric quantification, and morphometric analysis of brain scans. For the assessment of brain structure and integrity, CT is a non-invasive, cheaper, faster, and more widely available modality than MRI. However, the clinical application of CT is mostly limited to the visual assessment of brain integrity and exclusion of copathologies. We have previously developed two-dimensional (2D) deep learning-based segmentation networks that successfully classified brain tissue in head CT. Recently, deep learning-based MRI segmentation models successfully use patch-based three-dimensional (3D) segmentation networks. In this study, we aimed to develop patch-based 3D segmentation networks for CT brain tissue classification. Furthermore, we aimed to compare the performance of 2D- and 3D-based segmentation networks to perform brain tissue classification in anisotropic CT scans. For this purpose, we developed 2D and 3D U-Net-based deep learning models that were trained and validated on MR-derived segmentations from scans of 744 participants of the Gothenburg H70 Cohort with both CT and T1-weighted MRI scans acquired timely close to each other. Segmentation performance of both 2D and 3D models was evaluated on 234 unseen datasets using measures of distance, spatial similarity, and tissue volume. Single-task slice-wise processed 2D U-Nets performed better than multitask patch-based 3D U-Nets in CT brain tissue classification. These findings provide support to the use of 2D U-Nets to segment brain tissue in one-dimensional (1D) CT. This could increase the application of CT to detect brain abnormalities in clinical settings.

scholarly journals Siamese Reconstruction Network: Accurate Image Reconstruction from Human Brain Activity by Learning to Compare

2019 ◽  
Vol 9 (22) ◽  
pp. 4749
Author(s):  
Lingyun Jiang ◽  
Kai Qiao ◽  
Linyuan Wang ◽  
Chi Zhang ◽  
Jian Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Human Brain ◽  
Brain Activity ◽  
Feature Space ◽  
Training Data ◽  
Reconstruction Method ◽  
Learning Method ◽  
Training Samples ◽  
Visual Reconstruction ◽  
Relationship Of

Decoding human brain activities, especially reconstructing human visual stimuli via functional magnetic resonance imaging (fMRI), has gained increasing attention in recent years. However, the high dimensionality and small quantity of fMRI data impose restrictions on satisfactory reconstruction, especially for the reconstruction method with deep learning requiring huge amounts of labelled samples. When compared with the deep learning method, humans can recognize a new image because our human visual system is naturally capable of extracting features from any object and comparing them. Inspired by this visual mechanism, we introduced the mechanism of comparison into deep learning method to realize better visual reconstruction by making full use of each sample and the relationship of the sample pair by learning to compare. In this way, we proposed a Siamese reconstruction network (SRN) method. By using the SRN, we improved upon the satisfying results on two fMRI recording datasets, providing 72.5% accuracy on the digit dataset and 44.6% accuracy on the character dataset. Essentially, this manner can increase the training data about from n samples to 2n sample pairs, which takes full advantage of the limited quantity of training samples. The SRN learns to converge sample pairs of the same class or disperse sample pairs of different class in feature space.

scholarly journals RBIO-06. IMPLEMENTATION OF HUMAN INDUCED PLURIPOTENT STEM CELL DERIVED CEREBRAL ORGANOIDS TO MODEL NORMAL TISSUE RADIORESPONSE

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii193-ii193
Author(s):  
Lawrence Bronk ◽  
Sanjay Singh ◽  
Riya Thomas ◽  
Luke Parkitny ◽  
Mirjana Maletic-Savatic ◽  
...  
Keyword(s):  
Stem Cell ◽  
Human Brain ◽  
Brain Tissue ◽  
Human Tissue ◽  
Model Systems ◽  
Human Brain Tissue ◽  
Post Irradiation ◽  
Mature Neurons ◽  
Induced Pluripotent ◽  
Effects Of Radiation

Abstract Treatment-related sequelae following cranial irradiation have life changing impacts for patients and their caregivers. Characterization of the basic response of human brain tissue to irradiation has been difficult due to a lack of preclinical models. The direct study of human brain tissue in vitro is becoming possible due to advances in stem cell biology, neuroscience, and tissue engineering with the development of organoids as novel model systems which enable experimentation with human tissue models. We sought to establish a cerebral organoid (CO) model to study the radioresponse of normal human brain tissue. COs were grown using human induced pluripotent stem cells and a modified Lancaster protocol. Compositional analysis during development of the COs showed expected populations of neurons and glia. We confirmed a population of microglia-like cells within the model positive for the makers Iba1 and CD68. After 2-months of maturation, COs were irradiated to 0, 10, and 20 Gy using a Shepard Mark-II Cs-137 irradiator and returned to culture. Subsets of COs were prepared for immunostaining at 30- and 70-days post-irradiation. To examine the effect of irradiation on the neural stem cell (NSC) population, sections were stained for SOX2 and Ki-67 expression denoting NSCs and proliferation respectively. Slides were imaged and scored using the CellProfiler software package. The percentage of proliferating NSCs 30-days post-irradiation was found to be significantly reduced for irradiated COs (5.7% (P=0.007) and 3.4% (P=0.001) for 10 and 20 Gy respectively) compared to control (12.7%). The reduction in the proliferating NSC population subsequently translated to a reduced population of NeuN-labeled mature neurons 70 days post-irradiation. The loss of proliferating NSCs and subsequent reduction in mature neurons demonstrates the long-term effects of radiation. Our initial results indicate COs will be a valuable model to study the effects of radiation therapy on normal and diseased human tissue.

Demonstration of α1A-and α1B-adrenoceptor binding sites in human brain tissue

1989 ◽  
Vol 169 (2-3) ◽  
pp. 325-328 ◽  
Author(s):  
Gerhard Gross ◽  
Gertraud Hanft ◽  
Hubertus M. Mehdorn
Keyword(s):  
Human Brain ◽  
Brain Tissue ◽  
Binding Sites ◽  
Human Brain Tissue

The lipopigments in human brain tissue necroses

1980 ◽  
Vol 52 (2) ◽  
pp. 147-151 ◽  
Author(s):  
R. Schr�der ◽  
B. Reinartz
Keyword(s):  
Human Brain ◽  
Brain Tissue ◽  
Human Brain Tissue
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