172 research outputs found
Whole slide image registration for the study of tumor heterogeneity
Consecutive thin sections of tissue samples make it possible to study local
variation in e.g. protein expression and tumor heterogeneity by staining for a
new protein in each section. In order to compare and correlate patterns of
different proteins, the images have to be registered with high accuracy. The
problem we want to solve is registration of gigapixel whole slide images (WSI).
This presents 3 challenges: (i) Images are very large; (ii) Thin sections
result in artifacts that make global affine registration prone to very large
local errors; (iii) Local affine registration is required to preserve correct
tissue morphology (local size, shape and texture). In our approach we compare
WSI registration based on automatic and manual feature selection on either the
full image or natural sub-regions (as opposed to square tiles). Working with
natural sub-regions, in an interactive tool makes it possible to exclude
regions containing scientifically irrelevant information. We also present a new
way to visualize local registration quality by a Registration Confidence Map
(RCM). With this method, intra-tumor heterogeneity and charateristics of the
tumor microenvironment can be observed and quantified.Comment: MICCAI2018 - Computational Pathology and Ophthalmic Medical Image
Analysis - COMPA
Machine Learning Based Analytics for the Significance of Gait Analysis in Monitoring and Managing Lower Extremity Injuries
This study explored the potential of gait analysis as a tool for assessing
post-injury complications, e.g., infection, malunion, or hardware irritation,
in patients with lower extremity fractures. The research focused on the
proficiency of supervised machine learning models predicting complications
using consecutive gait datasets. We identified patients with lower extremity
fractures at an academic center. Patients underwent gait analysis with a
chest-mounted IMU device. Using software, raw gait data was preprocessed,
emphasizing 12 essential gait variables. Machine learning models including
XGBoost, Logistic Regression, SVM, LightGBM, and Random Forest were trained,
tested, and evaluated. Attention was given to class imbalance, addressed using
SMOTE. We introduced a methodology to compute the Rate of Change (ROC) for gait
variables, independent of the time difference between gait analyses. XGBoost
was the optimal model both before and after applying SMOTE. Prior to SMOTE, the
model achieved an average test AUC of 0.90 (95% CI: [0.79, 1.00]) and test
accuracy of 86% (95% CI: [75%, 97%]). Feature importance analysis attributed
importance to the duration between injury and gait analysis. Data patterns
showed early physiological compensations, followed by stabilization phases,
emphasizing prompt gait analysis. This study underscores the potential of
machine learning, particularly XGBoost, in gait analysis for orthopedic care.
Predicting post-injury complications, early gait assessment becomes vital,
revealing intervention points. The findings support a shift in orthopedics
towards a data-informed approach, enhancing patient outcomes.Comment: 13 pages, 6 figure
Convolutional neural network-based clinical predictors of oral dysplasia: class activation map analysis of deep learning results
Oral cancer/oral squamous cell carcinoma is among the top ten most common cancers globally, with over 500,000 new cases and 350,000 associated deaths every year worldwide. There is a critical need for objective, novel technologies that facilitate early, accurate diagnosis. For this purpose, we have developed a method to classify images as âsuspiciousâ and ânormalâ by performing transfer learning on Inception-ResNet-V2 and generated automated heat maps to highlight the region of the images most likely to be involved in decision making. We have tested the developed methodâs feasibility on two independent datasets of clinical photographic images of 30 and 24 patients from the UK and Brazil, respectively. Both 10-fold cross-validation and leave-one-patient-out validation methods were performed to test the system, achieving accuracies of 73.6% (±19%) and 90.9% (±12%), F1-scores of 97.9% and 87.2%, and precision values of 95.4% and 99.3% at recall values of 100.0% and 81.1% on these two respective cohorts. This study presents several novel findings and approaches, namely the development and validation of our methods on two datasets collected in different countries showing that using patches instead of the whole lesion image leads to better performance and analyzing which regions of the images are predictive of the classes using class activation map analysis
Segmentation of epidermal tissue with histopathological damage in images of haematoxylin and eosin stained human skin.
Background: Digital image analysis has the potential to address issues surrounding traditional histological techniques including a lack of objectivity and high variability, through the application of quantitative analysis. A key initial step in image analysis is the identification of regions of interest. A widely applied methodology is that of segmentation. This paper proposes the application of image analysis techniques to segment skin tissue with varying degrees of histopathological damage. The segmentation of human tissue is challenging as a consequence of the complexity of the tissue structures and inconsistencies in tissue preparation, hence there is a need for a new robust method with the capability to handle the additional challenges materialising from histopathological damage.Methods: A new algorithm has been developed which combines enhanced colour information, created following a transformation to the L*a*b* colourspace, with general image intensity information. A colour normalisation step is included to enhance the algorithm's robustness to variations in the lighting and staining of the input images. The resulting optimised image is subjected to thresholding and the segmentation is fine-tuned using a combination of morphological processing and object classification rules. The segmentation algorithm was tested on 40 digital images of haematoxylin & eosin (H&E) stained skin biopsies. Accuracy, sensitivity and specificity of the algorithmic procedure were assessed through the comparison of the proposed methodology against manual methods.Results: Experimental results show the proposed fully automated methodology segments the epidermis with a mean specificity of 97.7%, a mean sensitivity of 89.4% and a mean accuracy of 96.5%. When a simple user interaction step is included, the specificity increases to 98.0%, the sensitivity to 91.0% and the accuracy to 96.8%. The algorithm segments effectively for different severities of tissue damage.Conclusions: Epidermal segmentation is a crucial first step in a range of applications including melanoma detection and the assessment of histopathological damage in skin. The proposed methodology is able to segment the epidermis with different levels of histological damage. The basic method framework could be applied to segmentation of other epithelial tissues
Capturing Global Spatial Context for Accurate Cell Classification in Skin Cancer Histology
The spectacular response observed in clinical trials of immunotherapy in
patients with previously uncurable Melanoma, a highly aggressive form of skin
cancer, calls for a better understanding of the cancer-immune interface.
Computational pathology provides a unique opportunity to spatially dissect such
interface on digitised pathological slides. Accurate cellular classification is
a key to ensure meaningful results, but is often challenging even with
state-of-art machine learning and deep learning methods.
We propose a hierarchical framework, which mirrors the way pathologists
perceive tumour architecture and define tumour heterogeneity to improve cell
classification methods that rely solely on cell nuclei morphology. The SLIC
superpixel algorithm was used to segment and classify tumour regions in low
resolution H&E-stained histological images of melanoma skin cancer to provide a
global context. Classification of superpixels into tumour, stroma, epidermis
and lumen/white space, yielded a 97.7% training set accuracy and 95.7% testing
set accuracy in 58 whole-tumour images of the TCGA melanoma dataset. The
superpixel classification was projected down to high resolution images to
enhance the performance of a single cell classifier, based on cell nuclear
morphological features, and resulted in increasing its accuracy from 86.4% to
91.6%. Furthermore, a voting scheme was proposed to use global context as
biological a priori knowledge, pushing the accuracy further to 92.8%.
This study demonstrates how using the global spatial context can accurately
characterise the tumour microenvironment and allow us to extend significantly
beyond single-cell morphological classification.Comment: Accepted by MICCAI COMPAY 2018 worksho
The battle over Syria's reconstruction
Reconstruction is becoming the new battleground in the Syrian conflictâits continuation by other means. It is instrumentalized by the regime as a way to reconsolidate its control over the country and by rival regional and international powers to shape the internal balance of power and establish spheres of influence in the country. The paper examines the Asad regimeâs practices, including co-optation of militia leaders via reconstruction concessions and use of reconstruction to clear strategic areas of opposition-dominated urban settlements. The paper then surveys how the geopolitical struggle in Syria has produced an asymmetry as regards reconstruction: those powers that lost the geo-political contest on the ground seek to use geo-economic superiority to reverse the geo-political outcome. Then the impact of proxy wars and spheres of influence in the country on the security context for reconstruction is examined. Finally, the reconstruction initiatives of the various external parties are assessed, including Russia, Iran and Turkey as well as the spoiler role by which the US seeks to obstruct reconstruction that would spell victory in Syria for its Russian and Iranian rivals.PostprintPeer reviewe
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