Identificación automática de marcadores patológicos en imágenes de histopatología

Abstract. The inter and intra subject variability is a common problem in several tasks associated to the examination of histopathological samples. This variability might hinder the evaluation of cancerous diseases. The development of automatic image analysis techniques and computerized aided diagnostic tools in pathology aims to reduce the impact of such variability by offering quantitative measurements and estimations. These measurements allow an accurate evaluation and classification of the diseases in virtual slide images. The main problem addressed in this thesis is evaluating the correlation of the automated identification of pathological markers with cancer malignancy and aggresivenes. Hence, a set of classifier models are trained to detect known pathological patterns. The classifiers are then used to quantify the presence of the pathological markers. Finally, the resulting measurements are correlated with the cancer risk recurrence. Results show that the automated detectors are able to quantify patterns that show differences across several cancer risk groups.La variabilidad inter e intra sujeto es un problema frecuente en muchas tareas asociadas al ex´amen de muestras histopatológicas. Esta variabilidad puede incidir negativamente en la evaluación de patologías relacionadas con el cáncer. El desarrollo de técnicas para el análisis automático de imágenes y de herramientas de soporte al diagnóstico en patología tiene como objetivo reducir el impacto de la variabilidad inter/intra sujeto mediante la obtención de medidas y estimaciones cuantitativas. Estas medidas permiten una evaluación y clasificación más precisa de las enfermedades observables en l´aminas virtuales. El principal problema abordado en esta tesis consiste en evaluar la correlación de la identificación automática de marcadores patológicos con la agresividad del cáncer. As´ı, un conjunto de clasificadores son entrenados para detectar marcadores patológicos conocidos. Los clasificadores son posteriormente usados para cuantificar la presencia de los marcadores patológicos. Finalmente, las mediciones resultantes son correlacionadas con el riesgo de recurrencia del cáncer. Los resultados muestran que los detectores automáticos son capaces de cuantificar los patrones que muestran diferencias entre diferentes grupos de riesgo.Doctorad

Using CycleGANs for effectively reducing image variability across OCT devices and improving retinal fluid segmentation

Optical coherence tomography (OCT) has become the most important imaging modality in ophthalmology. A substantial amount of research has recently been devoted to the development of machine learning (ML) models for the identification and quantification of pathological features in OCT images. Among the several sources of variability the ML models have to deal with, a major factor is the acquisition device, which can limit the ML model's generalizability. In this paper, we propose to reduce the image variability across different OCT devices (Spectralis and Cirrus) by using CycleGAN, an unsupervised unpaired image transformation algorithm. The usefulness of this approach is evaluated in the setting of retinal fluid segmentation, namely intraretinal cystoid fluid (IRC) and subretinal fluid (SRF). First, we train a segmentation model on images acquired with a source OCT device. Then we evaluate the model on (1) source, (2) target and (3) transformed versions of the target OCT images. The presented transformation strategy shows an F1 score of 0.4 (0.51) for IRC (SRF) segmentations. Compared with traditional transformation approaches, this means an F1 score gain of 0.2 (0.12).Comment: * Contributed equally (order was defined by flipping a coin) --------------- Accepted for publication in the "IEEE International Symposium on Biomedical Imaging (ISBI) 2019

Prediction of navigation patterns in histopathological mega-images

La microscopía virtual puede mejorar el trabajo rutinario de los laboratorios patológicos modernos. Este objetivo ha sido severamente limitado por la gran cantidad de información contenida en las laminas histopatológicas virtuales. La adopción de técnicas para mejorar la eficiencia durante la navegación de mega-imágenes ha mostrado ser útil para reducir los tiempos de respuesta en sistemas de microscopía virtual. Este trabajo presenta un enfoque novedoso para predecir patrones de navegación en laminas histopatológicas virtuales durante tareas de evaluación diagnosticas realizadas por patólogos. A partir de la selección de imágenes de ejemplos positivos (objetivo) y negativos (distractor) realizada por el patólogo, el método construye un mapa asignando relevancia a cada una de las regiones de la mega-imagen. Durante la evaluación de la identificación de relevancia, se encontró que el método desarrollado presento medidas promedio de precisión (55 %) y de promedio de recall (38 %) en el conjunto de datos utilizado, superando otras técnicas para detectar regiones de interés basadas en modelos computacionales de atención visual (Modelo Itti). La información contenida en el mapa de relevancia mostro una capacidad predictiva útil para la formulación de estrategias optimas de navegación, superando estrategias tradicionales en algunas de las situaciones analizadas en el presente trabajo. / Abstract. Virtual microscopy can improve the work ow of modern pathology laboratories, a goal limited by the large size of the virtual slides (VS). Lately some strategies to accelerate the navigation performance in large images has reduced the time. This work presents a novel method for predicting navigation patterns in VS during diagnostic tasks performed by pathologists. By selecting positive and negative image examples, the method constructs a map that assigns relevance to each image region. The evaluation of the regions of interest through Precision-recall measurements, calculated at each step of any actual navigation, obtained an average precision of 55% and a recall of about 38% when using the available set of navigations, outperforming other techniques based on computational models of visual attention that identify regions of interest. The predictive capability of the elevancy map was useful in the formulation of strategies to improve navigation.Maestría en Ingeniería BiomédicaMaestrí

Adaptive Dimensionality Reduction with Semi-Supervision (AdDReSS): Classifying Multi-Attribute Biomedical Data.

Medical diagnostics is often a multi-attribute problem, necessitating sophisticated tools for analyzing high-dimensional biomedical data. Mining this data often results in two crucial bottlenecks: 1) high dimensionality of features used to represent rich biological data and 2) small amounts of labelled training data due to the expense of consulting highly specific medical expertise necessary to assess each study. Currently, no approach that we are aware of has attempted to use active learning in the context of dimensionality reduction approaches for improving the construction of low dimensional representations. We present our novel methodology, AdDReSS (Adaptive Dimensionality Reduction with Semi-Supervision), to demonstrate that fewer labeled instances identified via AL in embedding space are needed for creating a more discriminative embedding representation compared to randomly selected instances. We tested our methodology on a wide variety of domains ranging from prostate gene expression, ovarian proteomic spectra, brain magnetic resonance imaging, and breast histopathology. Across these various high dimensional biomedical datasets with 100+ observations each and all parameters considered, the median classification accuracy across all experiments showed AdDReSS (88.7%) to outperform SSAGE, a SSDR method using random sampling (85.5%), and Graph Embedding (81.5%). Furthermore, we found that embeddings generated via AdDReSS achieved a mean 35.95% improvement in Raghavan efficiency, a measure of learning rate, over SSAGE. Our results demonstrate the value of AdDReSS to provide low dimensional representations of high dimensional biomedical data while achieving higher classification rates with fewer labelled examples as compared to without active learning

Selection of mitotic and non-mitotic nuclei from the MITOS2012 dataset.

<p>A nuclei candidate detection algorithm is used and patches centered at each candidate centroid are extracted.</p

Evaluation of Maximum Query Efficiency.

<p><i>ϕ</i>^<i>MQE</i> describes the maximum efficiency in terms of queried labels given the same <i>ϕ</i>^<i>Acc</i> (shown as a dashed black line) between AdDReSS and SSAGE for (a) , (b) , (c) , and (d) .</p

Datasets used for evaluation.

<p>Datasets used for evaluation.</p

Evaluation of Variance for Silhouette Index.

<p>Variance of <i>ϕ</i>^<i>SI</i> at selected numbers of instances for which labels were revealed for AdDReSS, SSAGE, GE are shown for (a) , (b) , (c) , and (d) . GE shows zero variance as labeled information does not affect the embedding for GE.</p

Swiss Roll example.

<p>(a) 3D Swiss Roll with all labels revealed. (b) 3D Swiss Roll with initial labels <i>ℓ</i>(<i>S</i>_<i>tr</i>) revealed. (c) Initial 2D embedding with labels. (d) Initial 2D embedding with initial labels <i>ℓ</i>(<i>S</i>_<i>tr</i>). (e) Ambiguous samples (in blue) are determined via active learning. (f) Region of the Swiss Roll at the class boundary (region is shown as a box in (e)). Note the selection of ambiguous samples (in blue) at the boundary between the two classes (in red and green). (g) Subsequent 2D embedding incorporating newly queried labels from the ambiguous samples. (h) Region near the class boundaries (shown as a box from (g)) revealing the increased separation between the two classes (in red and green) following application of the AdDReSS scheme.</p

Evaluation of Classification Accuracy.

<p>Number of instances for which labels were revealed versus mean <i>ϕ</i>^<i>Acc</i> for AdDReSS, SSAGE, GE, and the maximum empirically derived <i>ϕ</i>^<i>Acc</i> across all runs is shown for (a) , (b) , (c) and (d) . Standard deviation of <i>ϕ</i>^<i>Acc</i> shown as error bounds at each <i>l</i>.</p