Machine learning techniques have shown great promise in digital pathology. However, a major bottleneck is the difficulty of annotating necessary amount of tissue to deal with several variability factors, namely chemical fixation, sample slicing, or staining. Usually, models are trained using sets of annotated small image patches, but then, the number of required patches may increase exponentially and yet they must represent such variability. This paper presents a method for automatic sample selection to train a classifier for ovarian cancer by integrating a novel soft clustering strategy. The method starts by classifying a large set of patches with a previously trained classifier and divide patches from the cancer class as highly and moderately confident. An unsupervised selection of moderately confident patches by a Probabilistic Latent Semantic Analysis (PLSA), picks samples from relevant and meaningful groups with maximum within-group variance. A new model is re-trained using the highly confident patches together with patches obtained from the associated PLSA. This strategy outperforms a model trained with a larger set of annotated patches while the training times and the number of samples are much more smaller. The strategy was evaluated in a set of patches from 18 patients with Serous Ovarian Cancer, obtaining a reduction of 54.62% in the training time and 73.66% in the number of samples, while recall rate improved from 0.69 to 0.73.
Diffusion-weighted imaging (DWI) is a magnetic resonance imaging technique commonly used to infer tissue microstructure, however, acquisition time requirements affect the effective spatial resolution for DWI high quality. This paper presents a novel super-resolution strategy to reconstruct high-resolution DW images by linearly combining information from different gradient acquisitions. The strategy comprises two main stages, a representation learning and a high-resolution mapping. In the former stage, information from different gradients is grouped by patch-wise statistical similarities. Representative coefficients are then estimated to represent each group. In the latter stage, adapted patch coefficients predict the high-resolution image while a regularization method eliminates possible reconstruction overlapping effects. Several tests evaluate the method ability to pre- dict high resolution information, PSNR and SSIM metrics were applied to quantitatively measure the quality improvement. Results demonstrate that quality reconstruction outperforms state of art methods in about 0.3 dB for PSNR and 1 % for SSIM.
Cardiac Magnetic Resonance (CMR) requires synchronization with the ECG to correct many types of noise. However, the complex heart motion frequently produces displaced slices that have to be either ignored or manually corrected since the ECG correction is useless in this case. This work presents a novel methodology that detects the motion artifacts in CMR using a saliency method that highlights the region where the heart chambers are located. Once the Region of Interest (RoI) is set, its center of gravity is determined for the set of slices composing the volume. The deviation of the gravity center is an estimation of the coherence between the slices and is used to find out slices with certain displacement. Validation was performed with distorted real images where a slice is artificially misaligned with respect to set of slices. The displaced slice is found with a Recall of 84% and F Score of 68%.
KEYWORDS: Magnetism, Magnetic resonance imaging, Distortion, Signal detection, Medical imaging, Image processing, Signal processing, Data modeling, Brain, Medicine
Magnetic resonance imaging (MRI) is widely used in medicine nowadays, yet a significant disadvantage is the amount of artifacts that affect the image during the acquisition process. This paper presents a strategy for automatic damage detection when the image is altered by movement or there is a loss of information due to magnetic susceptibility. This approach uses a conventional SV D to detect the variability between slices of the image and a region of damaged voxels within the volume. Using a simple derivative algorithm, the method was tested in several cases automatically revealing the distortion's location with a performance of 74% for slice damage and 55% for the volume's damaged region.
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