1,547 research outputs found
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
The Liver Tumor Segmentation Benchmark (LiTS)
In this work, we report the set-up and results of the Liver Tumor
Segmentation Benchmark (LITS) organized in conjunction with the IEEE
International Symposium on Biomedical Imaging (ISBI) 2016 and International
Conference On Medical Image Computing Computer Assisted Intervention (MICCAI)
2017. Twenty four valid state-of-the-art liver and liver tumor segmentation
algorithms were applied to a set of 131 computed tomography (CT) volumes with
different types of tumor contrast levels (hyper-/hypo-intense), abnormalities
in tissues (metastasectomie) size and varying amount of lesions. The submitted
algorithms have been tested on 70 undisclosed volumes. The dataset is created
in collaboration with seven hospitals and research institutions and manually
reviewed by independent three radiologists. We found that not a single
algorithm performed best for liver and tumors. The best liver segmentation
algorithm achieved a Dice score of 0.96(MICCAI) whereas for tumor segmentation
the best algorithm evaluated at 0.67(ISBI) and 0.70(MICCAI). The LITS image
data and manual annotations continue to be publicly available through an online
evaluation system as an ongoing benchmarking resource.Comment: conferenc
Three-dimensional semiautomatic liver segmentation method for non-contrast computed tomography based on a correlation map of locoregional histogram and probabilistic atlas
Background: We sought to evaluate a new regional segmentation method for use with three-dimensional (3D) non-contrast abdominal CT images and to report the preliminary results. Methods: The proposed method was evaluated in ten cases. Manually segmented areas were used as the gold standard for evaluation. To compare the standard and the extracted liver regions, the degree of coincidence R% was redefined by transforming a volumetric overlap error. We also evaluated the influence of varying the density window size in terms of setting the starting points. Results: We confirmed in ten cases that our method could segment the liver region more precisely than the conventional method. A size of window 15 voxels was optimal as the starting point in all cases. Conclusions: We demonstrated the accuracy of a 3D semiautomatic liver segmentation method for non-contrast CT. This method promises to offer radiologists a time-efficient segmentation aid.Yamaguchi S., Satake K., Yamaji Y., et al. Three-dimensional semiautomatic liver segmentation method for non-contrast computed tomography based on a correlation map of locoregional histogram and probabilistic atlas. Computers in Biology and Medicine 55, 79 (2014); https://doi.org/10.1016/j.compbiomed.2014.10.003
Statistical Shape Modelling and Segmentation of the Respiratory Airway
The human respiratory airway consists of the upper (nasal cavity, pharynx) and the lower (trachea, bronchi) respiratory tracts. Accurate segmentation of these two airway tracts can lead to better diagnosis and interpretation of airway-specific diseases, and lead to improvement in the localization of abnormal metabolic or pathological sites found within and/or surrounding the respiratory regions. Due to the complexity and the variability displayed in the anatomical structure of the upper respiratory airway along with the challenges in distinguishing the nasal cavity from non-respiratory regions such as the paranasal sinuses, it is difficult for existing algorithms to accurately segment the upper airway without manual intervention. This thesis presents an implicit non-parametric framework for constructing a statistical shape model (SSM) of the upper and lower respiratory tract, capable of distinct shape generation and be adapted for segmentation. An SSM of the nasal cavity was successfully constructed using 50 nasal CT scans. The performance of the SSM was evaluated for compactness, specificity and generality. An averaged distance error of 1.47 mm was measured for the generality assessment. The constructed SSM was further adapted with a modified locally constrained random walk algorithm to segment the nasal cavity. The proposed algorithm was evaluated on 30 CT images and outperformed comparative state-of-the-art and conventional algorithms. For the lower airway, a separate algorithm was proposed to automatically segment the trachea and bronchi, and was designed to tolerate the image characteristics inherent in low-contrast CT images. The algorithm was evaluated on 20 clinical low-contrast CT from PET-CT patient studies and demonstrated better performance (87.1±2.8 DSC and distance error of 0.37±0.08 mm) in segmentation results against comparative state-of-the-art algorithms
Automated Segmentation of Cerebral Aneurysm Using a Novel Statistical Multiresolution Approach
Cerebral Aneurysm (CA) is a vascular disease that threatens the lives of
many adults. It a ects almost 1:5 - 5% of the general population. Sub-
Arachnoid Hemorrhage (SAH), resulted by a ruptured CA, has high rates of
morbidity and mortality. Therefore, radiologists aim to detect it and diagnose
it at an early stage, by analyzing the medical images, to prevent or reduce its
damages.
The analysis process is traditionally done manually. However, with the
emerging of the technology, Computer-Aided Diagnosis (CAD) algorithms are
adopted in the clinics to overcome the traditional process disadvantages, as
the dependency of the radiologist's experience, the inter and intra observation
variability, the increase in the probability of error which increases consequently
with the growing number of medical images to be analyzed, and the artifacts
added by the medical images' acquisition methods (i.e., MRA, CTA, PET, RA,
etc.) which impedes the radiologist' s work.
Due to the aforementioned reasons, many research works propose di erent
segmentation approaches to automate the analysis process of detecting a CA
using complementary segmentation techniques; but due to the challenging task
of developing a robust reproducible reliable algorithm to detect CA regardless
of its shape, size, and location from a variety of the acquisition methods, a
diversity of proposed and developed approaches exist which still su er from
some limitations.
This thesis aims to contribute in this research area by adopting two promising
techniques based on the multiresolution and statistical approaches in the
Two-Dimensional (2D) domain. The rst technique is the Contourlet Transform
(CT), which empowers the segmentation by extracting features not apparent
in the normal image scale. While the second technique is the Hidden
Markov Random Field model with Expectation Maximization (HMRF-EM),
which segments the image based on the relationship of the neighboring pixels
in the contourlet domain.
The developed algorithm reveals promising results on the four tested Three-
Dimensional Rotational Angiography (3D RA) datasets, where an objective
and a subjective evaluation are carried out. For the objective evaluation, six
performance metrics are adopted which are: accuracy, Dice Similarity Index
(DSI), False Positive Ratio (FPR), False Negative Ratio (FNR), speci city,
and sensitivity. As for the subjective evaluation, one expert and four observers
with some medical background are involved to assess the segmentation visually.
Both evaluations compare the segmented volumes against the ground
truth data
Incorporating Cardiac Substructures Into Radiation Therapy For Improved Cardiac Sparing
Growing evidence suggests that radiation therapy (RT) doses to the heart and cardiac substructures (CS) are strongly linked to cardiac toxicities, though only the heart is considered clinically. This work aimed to utilize the superior soft-tissue contrast of magnetic resonance (MR) to segment CS, quantify uncertainties in their position, assess their effect on treatment planning and an MR-guided environment.
Automatic substructure segmentation of 12 CS was completed using a novel hybrid MR/computed tomography (CT) atlas method and was improved upon using a 3-dimensional neural network (U-Net) from deep learning. Intra-fraction motion due to respiration was then quantified. The inter-fraction setup uncertainties utilizing a novel MR-linear accelerator were also quantified. Treatment planning comparisons were performed with and without substructure inclusions and methods to reduce radiation dose to sensitive CS were evaluated. Lastly, these described technologies (deep learning U-Net) were translated to an MR-linear accelerator and a segmentation pipeline was created.
Automatic segmentations from the hybrid MR/CT atlas was able to generate accurate segmentations for the chambers and great vessels (Dice similarity coefficient (DSC) \u3e 0.75) but coronary artery segmentations were unsuccessful (DSC\u3c0.3). After implementing deep learning, DSC for the chambers and great vessels was ≥0.85 along with an improvement in the coronary arteries (DSC\u3e0.5). Similar accuracy was achieved when implementing deep learning for MR-guided RT. On average, automatic segmentations required ~10 minutes to generate per patient and deep learning only required 14 seconds. The inclusion of CS in the treatment planning process did not yield statistically significant changes in plan complexity, PTV, or OAR dose.
Automatic segmentation results from deep learning pose major efficiency and accuracy gains for CS segmentation offering high potential for rapid implementation into radiation therapy planning for improved cardiac sparing. Introducing CS into RT planning for MR-guided RT presented an opportunity for more effective sparing with limited increase in plan complexity
Incorporating Cardiac Substructures Into Radiation Therapy For Improved Cardiac Sparing
Growing evidence suggests that radiation therapy (RT) doses to the heart and cardiac substructures (CS) are strongly linked to cardiac toxicities, though only the heart is considered clinically. This work aimed to utilize the superior soft-tissue contrast of magnetic resonance (MR) to segment CS, quantify uncertainties in their position, assess their effect on treatment planning and an MR-guided environment.
Automatic substructure segmentation of 12 CS was completed using a novel hybrid MR/computed tomography (CT) atlas method and was improved upon using a 3-dimensional neural network (U-Net) from deep learning. Intra-fraction motion due to respiration was then quantified. The inter-fraction setup uncertainties utilizing a novel MR-linear accelerator were also quantified. Treatment planning comparisons were performed with and without substructure inclusions and methods to reduce radiation dose to sensitive CS were evaluated. Lastly, these described technologies (deep learning U-Net) were translated to an MR-linear accelerator and a segmentation pipeline was created.
Automatic segmentations from the hybrid MR/CT atlas was able to generate accurate segmentations for the chambers and great vessels (Dice similarity coefficient (DSC) \u3e 0.75) but coronary artery segmentations were unsuccessful (DSC\u3c0.3). After implementing deep learning, DSC for the chambers and great vessels was ≥0.85 along with an improvement in the coronary arteries (DSC\u3e0.5). Similar accuracy was achieved when implementing deep learning for MR-guided RT. On average, automatic segmentations required ~10 minutes to generate per patient and deep learning only required 14 seconds. The inclusion of CS in the treatment planning process did not yield statistically significant changes in plan complexity, PTV, or OAR dose.
Automatic segmentation results from deep learning pose major efficiency and accuracy gains for CS segmentation offering high potential for rapid implementation into radiation therapy planning for improved cardiac sparing. Introducing CS into RT planning for MR-guided RT presented an opportunity for more effective sparing with limited increase in plan complexity
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