777 research outputs found
Mid-sagittal plane detection for advanced physiological measurements in brain scans
Objective: The process of diagnosing many neurodegenerative diseases, such as Parkinson's and progressive supranuclear palsy, involves the study of brain magnetic resonance imaging (MRI) scans in order to identify and locate morphological markers that can highlight the health status of the subject. A fundamental step in the pre-processing and analysis of MRI scans is the identification of the mid-sagittal plane, which corresponds to the mid-brain and allows a coordinate reference system for the whole MRI scan set. Approach: To improve the identification of the mid-sagittal plane we have developed an algorithm in Matlab® based on the k-means clustering function. The results have been compared with the evaluation of four experts who manually identified the mid-sagittal plane and whose performances have been combined with a cognitive decisional algorithm in order to define a gold standard. Main results: The comparison provided a mean percentage error of 1.84%. To further refine the automatic procedure we trained a machine learning system using the results from the proposed algorithm and the gold standard. We tested this machine learning system and obtained results comparable to medical raters with a mean absolute error of 1.86 slices. Significance: The system is promising and could be directly incorporated into broader diagnostic support systems
blob loss: instance imbalance aware loss functions for semantic segmentation
Deep convolutional neural networks have proven to be remarkably effective in semantic segmentation tasks. Most popular loss functions were introduced targeting improved volumetric scores, such as the Sorensen Dice coefficient. By design, DSC can tackle class imbalance; however, it does not recognize instance imbalance within a class. As a result, a large foreground instance can dominate minor instances and still produce a satisfactory Sorensen Dice coefficient. Nevertheless, missing out on instances will lead to poor detection performance. This represents a critical issue in applications such as disease progression monitoring. For example, it is imperative to locate and surveil small-scale lesions in the follow-up of multiple sclerosis patients. We propose a novel family of loss functions, nicknamed blob loss, primarily aimed at maximizing instance-level detection metrics, such as F1 score and sensitivity. Blob loss is designed for semantic segmentation problems in which the instances are the connected components within a class. We extensively evaluate a DSC-based blob loss in five complex 3D semantic segmentation tasks featuring pronounced instance heterogeneity in terms of texture and morphology. Compared to soft Dice loss, we achieve 5 percent improvement for MS lesions, 3 percent improvement for liver tumor, and an average 2 percent improvement for Microscopy segmentation tasks considering F1 score
blob loss: instance imbalance aware loss functions for semantic segmentation
Deep convolutional neural networks have proven to be remarkably effective in
semantic segmentation tasks. Most popular loss functions were introduced
targeting improved volumetric scores, such as the Sorensen Dice coefficient. By
design, DSC can tackle class imbalance; however, it does not recognize instance
imbalance within a class. As a result, a large foreground instance can dominate
minor instances and still produce a satisfactory Sorensen Dice coefficient.
Nevertheless, missing out on instances will lead to poor detection performance.
This represents a critical issue in applications such as disease progression
monitoring. For example, it is imperative to locate and surveil small-scale
lesions in the follow-up of multiple sclerosis patients. We propose a novel
family of loss functions, nicknamed blob loss, primarily aimed at maximizing
instance-level detection metrics, such as F1 score and sensitivity. Blob loss
is designed for semantic segmentation problems in which the instances are the
connected components within a class. We extensively evaluate a DSC-based blob
loss in five complex 3D semantic segmentation tasks featuring pronounced
instance heterogeneity in terms of texture and morphology. Compared to soft
Dice loss, we achieve 5 percent improvement for MS lesions, 3 percent
improvement for liver tumor, and an average 2 percent improvement for
Microscopy segmentation tasks considering F1 score.Comment: 23 pages, 7 figures // corrected one mistake where it said beta
instead of alpha in the tex
Improving longitudinal spinal cord atrophy measurements for clinical trials in multiple sclerosis by using the generalised boundary shift integral (GBSI)
Spinal cord atrophy is a common and clinically relevant feature of multiple sclerosis (MS), and can be used to monitor disease progression and as an outcome measure in clinical trials. Spinal cord atrophy is conventionally estimated with segmentation-based methods (e.g., cross-sectional spinal cord area (CSA)), where spinal cord change is calculated indirectly by numerical difference between timepoints. In this thesis, I validated the generalised boundary shift integral (GBSI), as the first registration-based method for longitudinal spinal cord atrophy measurement. The GBSI registers the baseline and follow-up spinal cord scans in a common half-way space, to directly determine atrophy on the cord edges. First, on a test dataset (9 MS patients and 9 controls), I have found that GBSI presented with lower random measurement error, than CSA, reflected by lower standard deviation, coefficient of variation and median absolute deviation. Then, on multi-centre, multi-manufacturer, and multi–field‐strength scans (282 MS patients and 82 controls), I confirmed that GBSI provided lower measurement variability in all MS subtypes and controls, than CSA, resulting into better separation between MS patients and controls, improved statistical power, and reduced sample size estimates. Finally, on a phase 2 clinical trial (220 primary-progressive MS patients), I demonstrated that spinal cord atrophy measurements on GBSI could be obtained from brain scans, considering their quality and association with corresponding spinal cord MRI-derived measurements. Not least, 1-year spinal cord atrophy measurements on GBSI, but not CSA, were associated with upper and lower limb motor function. In conclusion, spinal cord atrophy on the GBSI had higher measurement precision and stronger clinical correlates, than the segmentation method, and could be derived from high-quality brain acquisitions. Longitudinal spinal cord atrophy on GBSI could become a gold standard for clinical trials including spinal cord atrophy as an outcome measure, but should remain a secondary outcome measure, until further advancements increase the ease of acquisition and processing
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
Neurologic Diagnostics in 2035: The Neurology Future Forecasting Series
Innovations and advances in technologies over the past few years have yielded faster and wider diagnostic applications to patients with neurologic diseases. This article focuses on the foreseeable developments of the diagnostic tools available to the neurologist in the next 15 years. Clinical judgment is and will remain the cornerstone of the diagnostic process, assisted by novel technologies, such as artificial intelligence and machine learning. Future neurologists must be educated to develop, cultivate, and rely on their clinical skills, while becoming familiar with novel, often complex, assistive technologies
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Deep learning assisted MRI guided attenuation correction in PET
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonPositron emission tomography (PET) is a unique imaging modality that provides physiological
and functional details of the tissue at the molecular level. However, the acquired PET images
have some limitations such as the attenuation. PET attenuation correction is an essential step to
obtain the full potential of PET quantification. With the wide use of hybrid PET/MR scanners,
magnetic resonance (MR) images are used to address the problem of PET attenuation correction.
The MR images segmentation is one simple and robust approach to create pseudo computed
tomography (CT) images, which are used to generate attenuation coefficient maps to correct the
PET attenuation. Recently, deep learning has been proposed and used as a promising technique
to efficiently perform MR and various medical images segmentation.
In this research work, deep learning guided segmentation approaches have been proposed
to enhance the bone class segmentation of MR brain images in order to generate accurate
pseudo-CT images. The first approach has introduced the combination of handcrafted features
with deep learning features to enrich the set of features. Multiresolution analysis techniques,
which generate multiscale and multidirectional coefficients of an image such as contourlet and
shearlet transforms, are applied and combined with deep convolutional neural network (CNN)
features. Different experiments have been conducted to investigate the number of selected
coefficients and the insertion location of the handcrafted features.
The second approach aims at reducing the segmentation algorithm’s complexity while
maintaining the segmentation performance. An attention based convolutional encode-decoder
network has been proposed to adaptively recalibrate the deep network features. This attention based
network consists of two different squeeze and excitation blocks that excite the features
spatially and channel wise. The two blocks are combined sequentially to decrease the number
of network’s parameters and reduces the model complexity. The third approach has been focuses on the application of transfer learning from different MR sequences such as T1 weighted (T1-w) and T2 weighted (T2-w) images. A
pretrained model with T1-w MR sequences is fine tuned to perform the segmentation of T2-w
images. Multiple fine tuning approaches and experiments have been conducted to study the best
fine tuning mechanism that is able to build an efficient segmentation model for both T1-w and
T2-w segmentation. Clinical datasets of fifty patients with different conditions and diagnosis have been
used to carry an objective evaluation to measure the segmentation performance of the results
obtained by the three proposed methods. The first and second approaches have been validated
with other studies in the literature that applied deep network based segmentation technique to
perform MR based attenuation correction for PET images. The proposed methods have shown
an enhancement in the bone segmentation with an increase of dice similarity coefficient (DSC)
from 0.6179 to 0.6567 using an ensemble of CNNs with an improvement percentage of 6.3%.
The proposed excitation-based CNN has decreased the model complexity by decreasing the
number of trainable parameters by more than 46% where less computing resources are required
to train the model. The proposed hybrid transfer learning method has shown its superiority to
build a multi-sequences (T1-w and T2-w) segmentation approach compared to other applied
transfer learning methods especially with the bone class where the DSC is increased from 0.3841
to 0.5393. Moreover, the hybrid transfer learning approach requires less computing time than
transfer learning using open and conservative fine tuning
Un modelo de atención visual para la detección de regiones de interés en imágenes radiológicas
La detección, segmentación y cuantificación de lesiones de esclerosis múltiple (MS) en imágenes de resonancia magnética (MRI) ha sido un área de estudio muy activa en las ´últimas dos décadas. Esto es debido la necesidad de correlacionar estas medidas con la efectividad de los tratamientos farmacológicos. Muchos métodos han sido desarrollados y la mayoría no son específicos para los diferentes tipos de lesiones, es decir que no pueden distinguir entre lesiones agudas y crónicas. Los médicos radiólogos por su parte son capaces de distinguir entre diferentes niveles de la enfermedad haciendo uso de las imágenes de resonancia magnética de diferentes tipos. La principal motivación de este trabajo es la de emular mediante un modelo computacional la percepción visual del radiólogo, haciendo uso de los principios fisiológicos del sistema visual. De esta manera logramos detectar satisfactoriamente las lesiones de esclerosis múltiple en imágenes de resonancia magnética del cerebro. Este tipo de análisis nos permite estudiar y mejorar el estudio de las redes neuronales al poder introducir información a priori.Abstract. The detection, segmentation and quantification of multiple sclerosis (MS) lesions on magnetic resonance images (MRI) has been a very active field for the last two decades because of the urge to correlate these measures with the e↵ectiveness of pharmacological treatment. A myriad of methods has been developed and most of these are non specific for the type of lesions, e.g. they do not di↵erentiate between acute and chronic lesions. On the other hand, radiologists are able to distinguish between several stages of the disease on di↵erent types of MRI images. The main motivation of the work presented here is to computationally emulate the visual perception of the radiologist by using modeling principles of the neuronal centers along the visual system. By using this approach we were able to successfully detect multiple sclerosis lesions in brain MRI. This type of approach allows us to study and improve the analysis of brain networks by introducing a priori informationMaestrí
Automated brain lesion segmentation in magnetic resonance images
In this thesis, we investigate the potential of automation in brain lesion segmentation in magnetic resonance images. We first develop a novel supervised method, which segments regions in magnetic resonance images using gated recurrent units, provided training data with pixel-wise annotations on what to segment is available. We improve on this method using the latest technical advances in the field of machine learning and insights on possible weaknesses of our method, and adapt it specifically for the task of lesion segmentation in the brain. We show the feasibility of our approach on multiple public benchmarks, consistently reaching positions at the top of the list of competing methods. Adapting our problem successfully to the problem of landmark localization, we show the generalizability of the approach. Moving away from large training cohorts with manual segmentations to data where it is only known that a certain pathology is present, we propose a weakly-supervised segmentation approach. Given a set of images with known pathology of a certain kind and a healthy reference set, our formulation can segment the difference of the two data distributions. Lastly, we show how information from already existing lesion maps can be extracted in a meaningful way by connecting lesions across time in longitudinal studies. We hence present a full tool set for the automated processing of lesions in magnetic resonance images
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