15,119 research outputs found
Multiple Sclerosis Detection in Multispectral Magnetic Resonance Images with Principal Components Analysis
This paper presents a local feature vector based method for automated Multiple Sclerosis (MS) lesion segmentation of multi spectral MRI data. Twenty datasets from MS patients with FLAIR, T1,T2, MD and FA data with expert annotations are available as training set from the MICCAI 2008 challenge on MS, and 24 test datasets. Our local feature vector method contains neighbourhood voxel intensities, histogram and MS probability atlas information. Principal Component Analysis(PCA) with log-likelihood ratio is used to classify each voxel. MRI suffers from intensity inhomogenities. We try to correct this 'bias field' with 3 methods: a genetic algorithm, edge preserving filtering and atlas based correction. A large observer variability exist between expert classifications, but the similarity scores between model and expert classifications are often lower. Our model gives the best classification results with raw data, because bias correction gives artifacts at the edges and flatten large MS lesions
Monte Carlo-based Noise Compensation in Coil Intensity Corrected Endorectal MRI
Background: Prostate cancer is one of the most common forms of cancer found
in males making early diagnosis important. Magnetic resonance imaging (MRI) has
been useful in visualizing and localizing tumor candidates and with the use of
endorectal coils (ERC), the signal-to-noise ratio (SNR) can be improved. The
coils introduce intensity inhomogeneities and the surface coil intensity
correction built into MRI scanners is used to reduce these inhomogeneities.
However, the correction typically performed at the MRI scanner level leads to
noise amplification and noise level variations. Methods: In this study, we
introduce a new Monte Carlo-based noise compensation approach for coil
intensity corrected endorectal MRI which allows for effective noise
compensation and preservation of details within the prostate. The approach
accounts for the ERC SNR profile via a spatially-adaptive noise model for
correcting non-stationary noise variations. Such a method is useful
particularly for improving the image quality of coil intensity corrected
endorectal MRI data performed at the MRI scanner level and when the original
raw data is not available. Results: SNR and contrast-to-noise ratio (CNR)
analysis in patient experiments demonstrate an average improvement of 11.7 dB
and 11.2 dB respectively over uncorrected endorectal MRI, and provides strong
performance when compared to existing approaches. Conclusions: A new noise
compensation method was developed for the purpose of improving the quality of
coil intensity corrected endorectal MRI data performed at the MRI scanner
level. We illustrate that promising noise compensation performance can be
achieved for the proposed approach, which is particularly important for
processing coil intensity corrected endorectal MRI data performed at the MRI
scanner level and when the original raw data is not available.Comment: 23 page
Changes in total choline concentration in the breast of healthy fertile young women in relation to menstrual cycle or use of oral contraceptives: a 3-T 1H-MRS study
BACKGROUND:
To evaluate changes in total choline (tCho) absolute concentration ([tCho]) in the breast of healthy fertile women in relation to menstrual cycle (MC) or use of oral contraceptives (OC).
METHODS:
After institutional review board approval, we prospectively evaluated 40 healthy fertile volunteers: 20 with physiological MC, aged 28 ± 3 years (mean ± standard deviation; nOC group); 20 using OC, aged 26 ± 3 years (OC group). Hormonal assays and water-suppressed single-voxel 3-T proton magnetic resonance spectroscopy (1H-MRS) were performed on MC days 7, 14, and 21 in the nOC group and only on MC day 14 in the OC group. [tCho] was measured versus an external phantom. Mann-Whitney U test and Spearman coefficient were used; data are given as median and interquartile interval.
RESULTS:
All spectra had good quality. In the nOC group, [tCho] (mM) did not change significantly during MC: 0.8 (0.3-2.4) on day 7, 0.9 (0.4-1.2) on day 14, and 0.4 (0.2-0.8) on day 21 (p = 0.963). In the OC group, [tCho] was 0.7 (0.2-1.7) mM. The between-groups difference was not significant on all days (p ≥ 0.411). All hormones except prolactin changed during MC (p ≤ 0.024). In the OC group, [tCho] showed a borderline correlation with estradiol (r = 0.458, p = 0.056), but no correlation with other hormones (p ≥ 0.128). In the nOC group, [tCho] negatively correlated with prolactin (r = -0.587, p = 0.006) on day 7; positive correlation was found with estradiol on day 14 (r = 0.679, p = 0.001).
CONCLUSIONS:
A tCho peak can be detected in the normal mammary gland using 3-T 1H-MRS. The [tCho] in healthy volunteers was 0.4-0.9 mM, constant over the MC and independent of OC use
Towards a New Science of a Clinical Data Intelligence
In this paper we define Clinical Data Intelligence as the analysis of data
generated in the clinical routine with the goal of improving patient care. We
define a science of a Clinical Data Intelligence as a data analysis that
permits the derivation of scientific, i.e., generalizable and reliable results.
We argue that a science of a Clinical Data Intelligence is sensible in the
context of a Big Data analysis, i.e., with data from many patients and with
complete patient information. We discuss that Clinical Data Intelligence
requires the joint efforts of knowledge engineering, information extraction
(from textual and other unstructured data), and statistics and statistical
machine learning. We describe some of our main results as conjectures and
relate them to a recently funded research project involving two major German
university hospitals.Comment: NIPS 2013 Workshop: Machine Learning for Clinical Data Analysis and
Healthcare, 201
Cancer diagnosis using deep learning: A bibliographic review
In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Border, Color and Diameter (ABCD) method, seven-point detection method, Menzies method, and pattern analysis. They are used regularly by doctors for cancer diagnosis, although they are not considered very efficient for obtaining better performance. Moreover, considering all types of audience, the basic evaluation criteria are also discussed. The criteria include the receiver operating characteristic curve (ROC curve), Area under the ROC curve (AUC), F1 score, accuracy, specificity, sensitivity, precision, dice-coefficient, average accuracy, and Jaccard index. Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis. Artificial intelligence and cancer diagnosis are gaining attention as a way to define better diagnostic tools. In particular, deep neural networks can be successfully used for intelligent image analysis. The basic framework of how this machine learning works on medical imaging is provided in this study, i.e., pre-processing, image segmentation and post-processing. The second part of this manuscript describes the different deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial models (GANs), deep autoencoders (DANs), restricted Boltzmann’s machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN), multi-instance learning convolutional neural network (MIL-CNN). For each technique, we provide Python codes, to allow interested readers to experiment with the cited algorithms on their own diagnostic problems. The third part of this manuscript compiles the successfully applied deep learning models for different types of cancers. Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer. The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements
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