215 research outputs found
Brain Tumor Segmentation and Identification Using Particle Imperialist Deep Convolutional Neural Network in MRI Images
For the past few years, segmentation for medical applications using Magnetic Resonance (MR) images is concentrated. Segmentation of Brain tumors using MRIpaves an effective platform to plan the treatment and diagnosis of tumors. Thus, segmentation is necessary to be improved, for a novel framework. The Particle Imperialist Deep Convolutional Neural Network (PI-Deep CNN) suggested framework is intended to address the problems with segmenting and categorizing the brain tumor. Using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) Algorithm, the input MRI brain image is segmented, and then features are extracted using the Scatter Local Neighborhood Structure (SLNS) descriptor. Combining the scattering transform and the Local Neighborhood Structure (LNS) descriptor yields the proposed descriptor. A suggested Particle Imperialist algorithm-trained Deep CNN is then used to achieve the tumor-level classification. Different levels of the tumor are classified by the classifier, including Normal without tumor, Abnormal, Malignant tumor, and Non-malignant tumor. The cell is identified as a tumor cell and is subjected to additional diagnostics, with the exception of the normal cells that are tumor-free. The proposed method obtained a maximum accuracy of 0.965 during the experimentation utilizing the BRATS database and performance measures
USE-Net: Incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets
Prostate cancer is the most common malignant tumors in men but prostate
Magnetic Resonance Imaging (MRI) analysis remains challenging. Besides whole
prostate gland segmentation, the capability to differentiate between the blurry
boundary of the Central Gland (CG) and Peripheral Zone (PZ) can lead to
differential diagnosis, since tumor's frequency and severity differ in these
regions. To tackle the prostate zonal segmentation task, we propose a novel
Convolutional Neural Network (CNN), called USE-Net, which incorporates
Squeeze-and-Excitation (SE) blocks into U-Net. Especially, the SE blocks are
added after every Encoder (Enc USE-Net) or Encoder-Decoder block (Enc-Dec
USE-Net). This study evaluates the generalization ability of CNN-based
architectures on three T2-weighted MRI datasets, each one consisting of a
different number of patients and heterogeneous image characteristics, collected
by different institutions. The following mixed scheme is used for
training/testing: (i) training on either each individual dataset or multiple
prostate MRI datasets and (ii) testing on all three datasets with all possible
training/testing combinations. USE-Net is compared against three
state-of-the-art CNN-based architectures (i.e., U-Net, pix2pix, and Mixed-Scale
Dense Network), along with a semi-automatic continuous max-flow model. The
results show that training on the union of the datasets generally outperforms
training on each dataset separately, allowing for both intra-/cross-dataset
generalization. Enc USE-Net shows good overall generalization under any
training condition, while Enc-Dec USE-Net remarkably outperforms the other
methods when trained on all datasets. These findings reveal that the SE blocks'
adaptive feature recalibration provides excellent cross-dataset generalization
when testing is performed on samples of the datasets used during training.Comment: 44 pages, 6 figures, Accepted to Neurocomputing, Co-first authors:
Leonardo Rundo and Changhee Ha
A review on a deep learning perspective in brain cancer classification
AWorld Health Organization (WHO) Feb 2018 report has recently shown that mortality rate due to brain or central nervous system (CNS) cancer is the highest in the Asian continent. It is of critical importance that cancer be detected earlier so that many of these lives can be saved. Cancer grading is an important aspect for targeted therapy. As cancer diagnosis is highly invasive, time consuming and expensive, there is an immediate requirement to develop a non-invasive, cost-effective and efficient tools for brain cancer characterization and grade estimation. Brain scans using magnetic resonance imaging (MRI), computed tomography (CT), as well as other imaging modalities, are fast and safer methods for tumor detection. In this paper, we tried to summarize the pathophysiology of brain cancer, imaging modalities of brain cancer and automatic computer assisted methods for brain cancer characterization in a machine and deep learning paradigm. Another objective of this paper is to find the current issues in existing engineering methods and also project a future paradigm. Further, we have highlighted the relationship between brain cancer and other brain disorders like stroke, Alzheimer’s, Parkinson’s, andWilson’s disease, leukoriaosis, and other neurological disorders in the context of machine learning and the deep learning paradigm
Multimodal and multicontrast image fusion via deep generative models
Recently, it has become progressively more evident that classic diagnostic
labels are unable to reliably describe the complexity and variability of
several clinical phenotypes. This is particularly true for a broad range of
neuropsychiatric illnesses (e.g., depression, anxiety disorders, behavioral
phenotypes). Patient heterogeneity can be better described by grouping
individuals into novel categories based on empirically derived sections of
intersecting continua that span across and beyond traditional categorical
borders. In this context, neuroimaging data carry a wealth of spatiotemporally
resolved information about each patient's brain. However, they are usually
heavily collapsed a priori through procedures which are not learned as part of
model training, and consequently not optimized for the downstream prediction
task. This is because every individual participant usually comes with multiple
whole-brain 3D imaging modalities often accompanied by a deep genotypic and
phenotypic characterization, hence posing formidable computational challenges.
In this paper we design a deep learning architecture based on generative models
rooted in a modular approach and separable convolutional blocks to a) fuse
multiple 3D neuroimaging modalities on a voxel-wise level, b) convert them into
informative latent embeddings through heavy dimensionality reduction, c)
maintain good generalizability and minimal information loss. As proof of
concept, we test our architecture on the well characterized Human Connectome
Project database demonstrating that our latent embeddings can be clustered into
easily separable subject strata which, in turn, map to different phenotypical
information which was not included in the embedding creation process. This may
be of aid in predicting disease evolution as well as drug response, hence
supporting mechanistic disease understanding and empowering clinical trials
Intelligent Analysis of Cerebral Magnetic Resonance Images: Extracting Relevant Information from Small Datasets
Tesis doctoral inédita leÃda en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de IngenierÃa Informática. Fecha de lectura : 21-09-2017Los metodos de machine learning aplicados a imagenes medicas se estan convirtiendo
en potentes herramientas para el analisis y diagnostico de pacientes. La
alta disponibilidad de repositorios de im agenes de diferentes modalidades ha favorecido
el desarrollo de sistemas que aprenden a extraer caracteristicas relevantes
y construyen modelos predictivos a partir de grandes cantidades de informacion,
por ejemplo, los metodos de deep learning. Sin embargo, el analisis de conjuntos
de imagenes provenientes de un menor numero de sujetos, como es el caso de las
imagenes adquiridas en entornos de investigacion cl nica y pre-cl nica, ha recibido
considerablemente menos atencion. El objetivo de esta tesis es implementar un conjunto
de herramientas avanzadas para resolver este problema, permitiendo el analisis
robusto de Im agenes de Resonancia Magn etica (MRI por sus siglas en ingl es) cuando
se dispone de pocos sujetos de estudio. En este contexto, las herramientas propuestas
se emplean para analizar de manera autom atica conjuntos de datos obtenidos
de imagenes funcionales de MR del cerebro en estudios de regulacion del apetito en
roedores y humanos, y de im agenes funcionales y estructurales de MR de desarrollos
tumorales en modelos animales y humanos. Los metodos propuestos se derivan de
la idea de considerar cada voxel del conjunto de im agenes como un patron, en lugar
de la nocion convencional de considerar cada imagen como un patr on.
El Cap tulo 1 describe la motivaci on de esta tesis, incluyendo los objetivos
propuestos, la estructura general del documento y las contribuciones de esta investigaci
on. El Capitulo 2 contiene una introduccion actualizada del estado del arte
en MRI, los procedimientos mas usados en el pre-procesamiento de imagenes, y los
algoritmos de machine learning m as utiles y sus aplicaciones en MRI. El Cap tulo
3 presenta el dise~no experimental y los pasos de pre-procesamiento aplicados a los
conjuntos de datos de regulaci on de apetito y desarrollo tumoral. El Capitulo 4 implementa
nuevos metodos de aprendizaje supervisados para el analisis de conjuntos
de datos de MRI obtenidos de un conjunto peque~no de sujetos. Se ilustra este enfoque
presentando primero la metodolog a Fisher Maps, que permite la visualizaci on
cuantitativa y no invasiva de la circuiter a cerebral del apetito, mediante el an alisis
autom atico de Im agenes Ponderadas en Difusi on (DWI por sus siglas en ingl es).
Esta metodolog a se extiende a la clasi caci on de im agenes completas combinando
las predicciones obtenidas de cada p xel. El Cap tulo 5 propone un nuevo algoritmo
de aprendizaje no supervisado, ilustrando su desempe~no sobre datos sint eticos
y datos provenientes de estudios de tumores cerebrales y crecimiento tumoral. Por
ultimo, en el Cap tulo 6 se resumen las principales conclusiones de este trabajo y
se plantean amplias v as para su desarrollo futuro.
En resumen, esta tesis presenta un nuevo enfoque capaz de trabajar en contextos
con baja disponibilidad de sujetos de estudio, proponiendo algoritmos de aprendizaje supervisado y no supervisado. Estos metodos pueden ser facilmente generalizados a
otros paradigmas o patologias, e incluso, a distintas modalidades de imagenes
Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates
The study of cerebral anatomy in developing neonates is of great importance for
the understanding of brain development during the early period of life. This
dissertation therefore focuses on three challenges in the modelling of cerebral
anatomy in neonates during brain development. The methods that have been
developed all use Magnetic Resonance Images (MRI) as source data.
To facilitate study of vascular development in the neonatal period, a set of image
analysis algorithms are developed to automatically extract and model cerebral
vessel trees. The whole process consists of cerebral vessel tracking from
automatically placed seed points, vessel tree generation, and vasculature
registration and matching. These algorithms have been tested on clinical Time-of-
Flight (TOF) MR angiographic datasets.
To facilitate study of the neonatal cortex a complete cerebral cortex segmentation
and reconstruction pipeline has been developed. Segmentation of the neonatal
cortex is not effectively done by existing algorithms designed for the adult brain
because the contrast between grey and white matter is reversed. This causes pixels
containing tissue mixtures to be incorrectly labelled by conventional methods. The
neonatal cortical segmentation method that has been developed is based on a novel
expectation-maximization (EM) method with explicit correction for mislabelled
partial volume voxels. Based on the resulting cortical segmentation, an implicit
surface evolution technique is adopted for the reconstruction of the cortex in
neonates. The performance of the method is investigated by performing a detailed
landmark study.
To facilitate study of cortical development, a cortical surface registration algorithm
for aligning the cortical surface is developed. The method first inflates extracted
cortical surfaces and then performs a non-rigid surface registration using free-form
deformations (FFDs) to remove residual alignment. Validation experiments using
data labelled by an expert observer demonstrate that the method can capture local
changes and follow the growth of specific sulcus
Individualised Clinical Neuroimaging in the Developing Brain: Abnormality Detection
Perinatal neuroanatomical structure is incredibly intricate and, at time of birth, is undergoing continuous change due to interweaving developmental processes (growth, myelination and gyrification). While there is some small variability in structure and rates of development, all follow proscribed pathways with well documented milestones. Brain injury or other disruption of these processes can result in poor neurodevelopmental outcomes or mortality, making their early identification critical to estimate, and potentially forestall, negative effects. MRI is an increasingly used method of investigating suspected neonatal encephalopathies and injuries.Identification of these injuries and malformations is more challenging in neonates compared to adults due to the brain’s continuously evolving appearance. This makes radiological review of neonatal MRI an intensive and time-consuming task which, in an ideal setting, requires a team of highly skilled clinicians and radiologists with complementary training and extensive experience. To assist this review process, some localisation method that highlights areas likely to contain tissue abnormalities would be highly desirable, as it could quickly draw attention to these locations. In addition, identifying neonates whose MRI is likely to contain some form of pathology could allow for review prioritisation.In this thesis, I first investigated using normative models of neonatal tissue intensity for brain tissue abnormality detection. I applied voxel-wise Gaussian process (GP) regression to a training cohort of neonates with no obvious lesions, all born preterm (<37 weeks) but imaged between 28-55 weeks. Gestational age at birth (GA), postmenstrual age at scan (PMA) and sex were used as input variables and voxel intensity as the output variable. GPs output a mean value and its variance inferred from neonates within the training cohort whose demographic information most closely matched those of the prediction target. The voxel specific models were put together to form a synthesised typical image and standard deviation image derived from the variance outputs. Z-score abnormality maps were constructed by taking the difference between neonates actual MRI and GP-calculated synthetic image and scaling by their standard deviation map. Higher Z-score map values indicate voxels more likely to contain abnormal tissue intensity. Using manually delineated masks of common brain injuries seen in a subset of neonates, these abnormality Z-score maps demonstrated good detection performance using area under the curve of receiver operating characteristic scores, with the exception of small punctate lesions.The initial voxel-wise models had substantial false positives around the edges of the brain where there is large typical heterogeneity. I next investigated if incorporating local structural information into predictive models could improve their ability to accommodate typical anatomical heterogeneity seen across individual brains and improve the accuracy of synthetic images and abnormality detection. To achieve this, voxel intensity values in a patch surrounding the prediction target were appended to the design matrix, alongside GMA, PMA and sex. The patch-based synthetic images were able to match an individual’s brain structure more closely and had lower false positives in normal appearing tissue. However, a weakness was that the centre of some larger lesions was included in the predictions (thereby classified as ‘healthy’ tissue), having a deleterious effect on their coverage, increasing false negatives. This was offset by much better coverage of smaller, more subtle lesions, to the extent that overall performance was higher compared to that seen in the earlier model.I also investigated if the Z-score abnormality maps could be used to classify neonates with MRI positive brain injury from those with normal appearing brains. While many machine learning algorism see frequent use in neuroimaging classification tasks, I opted for a logistic regression model due to its high levels of interpretability and simple implementation. Using the histograms of the Z-score abnormality maps as inputs, the model demonstrated good performance, being able to correctly identify neonates with injuries, but not those with subtle lesions like punctate lesions, whilst minimising false identification of neonates with normal appearing brains.To ascertain if performance could be improved, I explored multiple classification methods. Specifically, the use of other more complex classifiers (random forest, support vector machines, GP classification) and the use of a regional abnormal voxel count, that allowed localisation of lesioned tissue rather than the more global detection ability of the Z-score histograms. Using these innovations, I investigated their application towards a specific pathology; hypoxic ischemic encephalopathy (HIE). This is a good test for the system, as HIE has high incidence rates, multiple associated lesion types and a time dependant appearance. Further, I wanted to know if, given a positive HIE diagnosis, the Z-score abnormality maps could be used to predict long-term outcomes (normal vs poor). Several models demonstrated an excellent ability to separate HIE and healthy control neonates achieving >90% accuracy, a statistically significant result even after false discovery rate (FDR) correction (p-value < 0.05). While the outcome prediction models achieved reasonable accuracy, >70% in multiple models, none of these were statistically significant after FDR correction.Overall, this work demonstrates how normative modelling can be used to create individual voxel-wise / image-wise estimation of tissue abnormality for neonatal MRI across a range of gestational ages. It further demonstrates that these abnormality maps can be utilised for additional tasks, in this instance, three increasingly challenging neurological classification problems. These include the separation of neonates with and without MRI positive lesions, identification of neonates with a specific pathological condition (HIE) and prediction of long-term functional outcome (normal vs poor). Within a radiological setting, these classifications task can be considered analogous to three radiological challenges, image triage, diagnostic detection and estimation of developmental prognosis, important for the clinical team but also infants and their families
Methods for the analysis and characterization of brain morphology from MRI images
Brain magnetic resonance imaging (MRI) is an imaging modality that produces
detailed images of the brain without using any ionizing radiation.
From a structural MRI scan, it is possible to extract morphological properties
of different brain regions, such as their volume and shape. These measures
can both allow a better understanding of how the brain changes due
to multiple factors (e.g., environmental and pathological) and contribute to
the identification of new imaging biomarkers of neurological and psychiatric
diseases. The overall goal of the present thesis is to advance the knowledge
on how brain MRI image processing can be effectively used to analyze and
characterize brain structure.
The first two works presented in this thesis are animal studies that primarily
aim to use MRI data for analyzing differences between groups of
interest. In Paper I, MRI scans from wild and domestic rabbits were processed
to identify structural brain differences between these two groups.
Domestication was found to significantly reshape brain structure in terms
of both regional gray matter volume and white matter integrity. In Paper II,
rat brain MRI scans were used to train a brain age prediction model. This
model was then tested on both controls and a group of rats that underwent
long-term environmental enrichment and dietary restriction. This healthy
lifestyle intervention was shown to significantly affect the predicted brain
age trajectories by slowing the rats’ aging process compared to controls.
Furthermore, brain age predicted on young adult rats was found to have a
significant effect on survival.
Papers III to V are human studies that propose deep learning-based
methods for segmenting brain structures that can be severely affected by
neurodegeneration. In particular, Papers III and IV focus on U-Net-based
2D segmentation of the corpus callosum (CC) in multiple sclerosis (MS)
patients. In both studies, good segmentation accuracy was obtained and a
significant correlation was found between CC area and the patient’s level of
cognitive and physical disability. Additionally, in Paper IV, shape analysis
of the segmented CC revealed a significant association between disability
and both CC thickness and bending angle. Conversely, in Paper V, a novel
method for automatic segmentation of the hippocampus is proposed, which
consists of embedding a statistical shape prior as context information into
a U-Net-based framework. The inclusion of shape information was shown
to significantly improve segmentation accuracy when testing the method
on a new unseen cohort (i.e., different from the one used for training).
Furthermore, good performance was observed across three different diagnostic
groups (healthy controls, subjects with mild cognitive impairment
and Alzheimer’s patients) that were characterized by different levels of hippocampal
atrophy.
In summary, the studies presented in this thesis support the great value
of MRI image analysis for the advancement of neuroscientific knowledge,
and their contribution is mostly two-fold. First, by applying well-established
processing methods on datasets that had not yet been explored in the literature,
it was possible to characterize specific brain changes and disentangle
relevant problems of a clinical or biological nature. Second, a technical
contribution is provided by modifying and extending already-existing brain
image processing methods to achieve good performance on new datasets
- …