1,075 research outputs found
A Novel Self-Learning Framework for Bladder Cancer Grading Using Histopathological Images
Recently, bladder cancer has been significantly increased in terms of
incidence and mortality. Currently, two subtypes are known based on tumour
growth: non-muscle invasive (NMIBC) and muscle-invasive bladder cancer (MIBC).
In this work, we focus on the MIBC subtype because it is of the worst prognosis
and can spread to adjacent organs. We present a self-learning framework to
grade bladder cancer from histological images stained via immunohistochemical
techniques. Specifically, we propose a novel Deep Convolutional Embedded
Attention Clustering (DCEAC) which allows classifying histological patches into
different severity levels of the disease, according to the patterns established
in the literature. The proposed DCEAC model follows a two-step fully
unsupervised learning methodology to discern between non-tumour, mild and
infiltrative patterns from high-resolution samples of 512x512 pixels. Our
system outperforms previous clustering-based methods by including a
convolutional attention module, which allows refining the features of the
latent space before the classification stage. The proposed network exceeds
state-of-the-art approaches by 2-3% across different metrics, achieving a final
average accuracy of 0.9034 in a multi-class scenario. Furthermore, the reported
class activation maps evidence that our model is able to learn by itself the
same patterns that clinicians consider relevant, without incurring prior
annotation steps. This fact supposes a breakthrough in muscle-invasive bladder
cancer grading which bridges the gap with respect to train the model on
labelled data
Automatic Detection of Intestinal Content to Evaluate Visibility in Capsule Endoscopy
In capsule endoscopy (CE), preparation of the small bowel before the procedure is believed to increase visibility of the mucosa for analysis. However, there is no consensus on the best method of preparation, while comparison is difficult due to the absence of an objective automated evaluation method.
The method presented here aims to fill this gap by automatically detecting regions in frames of CE videos where the mucosa is covered by bile, bubbles and remainders of food. We implemented two different machine learning techniques for supervised classification of patches: one based on hand-crafted feature extraction and Support Vector Machine classification and the other based on fine-tuning different convolutional neural network (CNN) architectures, concretely VGG-16 and VGG-19.
Using a data set of approximately 40,000 image patches obtained from 35 different patients, our best model achieved an average detection accuracy of 95.15% on our test patches, which is similar to significantly more complex detection methods used for similar purposes. We then estimate the probabilities at a pixel level by interpolating the patch probabilities and extract statistics from these, both on per-frame and per-video basis, intended for comparison of different videos.This work was funded by the European Union’s H2020: MSCA: ITN
program for the “Wireless In-body Environment Communication – WiBEC”
project under the grant agreement no. 675353.Noorda, R.; Nevárez, A.; Colomer, A.; Naranjo, V.; Pons Beltrán, V. (2020). Automatic Detection of Intestinal Content to Evaluate Visibility in Capsule Endoscopy. IEEE. 163-168. https://doi.org/10.1109/ISMICT.2019.8743878S16316
Metal artifact reduction in dental CT images using polar mathematical morphology
Most dental implant planning systems use a 3D representation of the CT scan of the patient under study as it provides a more intuitive view of the human jaw. The presence of metallic objects in human jaws, such as amalgam or gold fillings, provokes several artifacts like streaking and beam hardening which makes the reconstruction process difficult. In order to reduce these artifacts, several methods have been proposed using the raw data, directly obtained from the tomographs, in different ways. However, in DICOM-based applications this information is not available, and thus the need of a new method that handles this task in the DICOM domain. The presented method performs a morphological filtering in the polar domain yielding output images less affected by artifacts (even in cases of multiple metallic objects) without causing significant smoothing of the anatomic structures, which allows a great improvement in the 3D reconstruction. The algorithm has been automated and compared to other image denoising methods with successful results. (C) 2010 Elsevier Ireland Ltd. All rights reserved.This work has been supported by the project MIRACLE (DPI2007-66782-C03-01-AR07) of Spanish Ministerio de Educacion y Ciencia.Naranjo Ornedo, V.; Llorens RodrĂguez, R.; Alcañiz Raya, ML.; LĂłpez-Mir, F. (2011). Metal artifact reduction in dental CT images using polar mathematical morphology. Computer Methods and Programs in Biomedicine. 102(1):64-74. https://doi.org/10.1016/j.cmpb.2010.11.009S6474102
Design and Development of an Automatic Blood Detection System for Capsule Endoscopy Images
Wireless Capsule Endoscopy is a technique that allows for
observation of the entire gastrointestinal tract in an easy and non-invasive
way. However, its greatest limitation lies in the time required to analyze
the large number of images generated in each examination for diagnosis,
which is about 2 hours. This causes not only a high cost, but also a high
probability of a wrong diagnosis due to the physician’s fatigue, while the
variable appearance of abnormalities requires continuous concentration.
In this work, we designed and developed a system capable of automatically detecting blood based on classification of extracted regions, following two different classification approaches. The first method consisted
in extraction of hand-crafted features that were used to train machine
learning algorithms, specifically Support Vector Machines and Random
Forest, to create models for classifying images as healthy tissue or blood.
The second method consisted in applying deep learning techniques, concretely convolutional neural networks, capable of extracting the relevant
features of the image by themselves. The best results (95.7% sensitivity
and 92.3% specificity) were obtained for a Random Forest model trained
with features extracted from the histograms of the three HSV color space
channels. For both methods we extracted square patches of several sizes
using a sliding window, while for the first approach we also implemented
the waterpixels technique in order to improve the classification resultsThis work was funded by the European Unions H2020:
MSCA: ITN program for the “Wireless In-body Environment Communication
WiBEC” project under the grant agreement no. 675353. Additionally, we gratefully acknowledge the support of NVIDIA Corporation with the donation of the
Titan V GPU used for this research.Pons Suñer, P.; Noorda, R.; Nevárez, A.; Colomer, A.; Pons Beltrán, V.; Naranjo, V. (2019). Design and Development of an Automatic Blood Detection System for Capsule Endoscopy Images. En Lecture Notes in Artificial Intelligence. Springer. 105-113. https://doi.org/10.1007/978-3-030-33617-2_12S105113Berens, J., Finlayson, G.D., Qiu, G.: Image indexing using compressed colour histograms. IEE Proc. Vis., Image Signal Process. 147(4), 349–355 (2000). https://doi.org/10.1049/ip-vis:20000630Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001). https://doi.org/10.1023/A:1010933404324Buscaglia, J.M., et al.: Performance characteristics of the suspected blood indicator feature in capsule endoscopy according to indication for study. Clin. Gastroenterol. Hepatol. 6(3), 298–301 (2008). https://doi.org/10.1016/j.cgh.2007.12.029Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995). https://doi.org/10.1007/BF00994018Li, B., Meng, M.Q.H.: Computer-aided detection of bleeding regions for capsule endoscopy images. IEEE Trans. Biomed. Eng. 56(4), 1032–1039 (2009). https://doi.org/10.1109/TBME.2008.2010526Machairas, V., Faessel, M., Cárdenas-Peña, D., Chabardes, T., Walter, T., Decencière, E.: Waterpixels. IEEE Trans. Image Process. 24(11), 3707–3716 (2015). https://doi.org/10.1109/TIP.2015.2451011Novozámskỳ, A., Flusser, J., TachecĂ, I., SulĂk, L., Bureš, J., Krejcar, O.: Automatic blood detection in capsule endoscopy video. J. Biomed. Opt. 21(12), 126007 (2016). https://doi.org/10.1117/1.JBO.21.12.126007Signorelli, C., Villa, F., Rondonotti, E., Abbiati, C., Beccari, G., de Franchis, R.: Sensitivity and specificity of the suspected blood identification system in video capsule enteroscopy. Endoscopy 37(12), 1170–1173 (2005). https://doi.org/10.1055/s-2005-870410Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)Varma, S., Simon, R.: Bias in error estimation when using cross-validation for model selection. BMC Bioinform. 7(1), 91 (2006). https://doi.org/10.1186/1471-2105-7-9
Circumpapillary OCT-Focused Hybrid Learning for Glaucoma Grading Using Tailored Prototypical Neural Networks
Glaucoma is one of the leading causes of blindness worldwide and Optical
Coherence Tomography (OCT) is the quintessential imaging technique for its
detection. Unlike most of the state-of-the-art studies focused on glaucoma
detection, in this paper, we propose, for the first time, a novel framework for
glaucoma grading using raw circumpapillary B-scans. In particular, we set out a
new OCT-based hybrid network which combines hand-driven and deep learning
algorithms. An OCT-specific descriptor is proposed to extract hand-crafted
features related to the retinal nerve fibre layer (RNFL). In parallel, an
innovative CNN is developed using skip-connections to include tailored residual
and attention modules to refine the automatic features of the latent space. The
proposed architecture is used as a backbone to conduct a novel few-shot
learning based on static and dynamic prototypical networks. The k-shot paradigm
is redefined giving rise to a supervised end-to-end system which provides
substantial improvements discriminating between healthy, early and advanced
glaucoma samples. The training and evaluation processes of the dynamic
prototypical network are addressed from two fused databases acquired via
Heidelberg Spectralis system. Validation and testing results reach a
categorical accuracy of 0.9459 and 0.8788 for glaucoma grading, respectively.
Besides, the high performance reported by the proposed model for glaucoma
detection deserves a special mention. The findings from the class activation
maps are directly in line with the clinicians' opinion since the heatmaps
pointed out the RNFL as the most relevant structure for glaucoma diagnosis
Removing interference components in time frequency representations using morphological operators
Time-frequency representations have been of great interest in the analysis and classification of non-stationary signals. The use of highly selective transformation techniques is a valuable tool for obtaining accurate information for studies of this type. The Wigner-Ville distribution has high time and frequency selectivity in addition to meeting some interesting mathematical properties. However, due to the bi-linearity of the transform, interference terms emerge when the transform is applied over multi-component signals. In this paper, we propose a technique to remove cross-components from the Wigner-Ville transform using image processing algorithms. The proposed method exploits the advantages of non-linear morphological filters, using a spectrogram to obtain an adequate marker for the morphological processing of the Wigner-Ville transform. Unlike traditional smoothing techniques, this algorithm provides cross-term attenuations while preserving time-frequency resolutions. Moreover, it could also be applied to distributions with different interference geometries. The method has been applied to a set of different time-frequency transforms, with promising results. © 2011 Elsevier Inc. All rights reserved.This work was supported by the National R&D Program under Grant TEC2008-02975 (Spain), FEDER programme and Generalitat Valenciana CMAP 340.GĂłmez GarcĂa, S.; Naranjo Ornedo, V.; Miralles RicĂłs, R. (2011). Removing interference components in time frequency representations using morphological operators. Journal of Visual Communication and Image Representation. 22(1):401-410. doi:10.1016/j.jvcir.2011.03.007S40141022
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