Fluoroscopy-based tracking of femoral kinematics with statistical shape models

Precise knee kinematics assessment helps to diagnose knee pathologies and to improve the design of customized prosthetic components. The first step in identifying knee kinematics is to assess the femoral motion in the anatomical frame. However, no work has been done on pathological femurs, whose shape can be highly different from healthy ones

Wavelet appearance pyramids for landmark detection and pathology classification : application to lumbar spinal stenosis

Appearance representation and feature extraction of anatomy or anatomical features is a key step for segmentation and classification tasks. We focus on an advanced appearance model in which an object is decomposed into pyramidal complementary channels, and each channel is represented by a part-based model. We apply it to landmark detection and pathology classification on the problem of lumbar spinal stenosis. The performance is evaluated on 200 routine clinical data with varied pathologies. Experimental results show an improvement on both tasks in comparison with other appearance models. We achieve a robust landmark detection performance with average point to boundary distances lower than 2 pixels, and image-level anatomical classification with accuracies around 85%

Model-order selection in statistical shape models

Statistical shape models enhance machine learning algorithms providing prior information about deformation. A Point Distribution Model (PDM) is a popular landmark-based statistical shape model for segmentation. It requires choosing a model order, which determines how much of the variation seen in the training data is accounted for by the PDM. A good choice of the model order depends on the number of training samples and the noise level in the training data set. Yet the most common approach for choosing the model order simply keeps a predetermined percentage of the total shape variation. In this paper, we present a technique for choosing the model order based on information-theoretic criteria, and we show empirical evidence that the model order chosen by this technique provides a good trade-off between over- and underfitting.Comment: To appear in 2018 IEEE International Workshop on Machine Learning for Signal Processing, Sept.\ 17--20, 2018, Aalborg, Denmar

Detección automática de tejido blando nasal en CT-Scan y MRI utilizando Random Forests

Este trabajo presenta un procedimiento sencillo para la detección automática de tejido blando nasal a partir de imágenes médicas de tipo CT-Scan y MRI. Para dichas imágenes, inicialmente se aplica un procedimiento de segmentación manual y técnicas de procesamiento digital de imágenes para la confección de un dataset de entrenamiento, conteniendo los valores de cada pixel de la imagen por un lado y 4 valores que representan el área de interés a detectar por otro. Posteriormente, se utilizan métodos de preprocesamiento de datos sobre estas imágenes para ser utilizadas como conjunto de entrenamiento, validación y prueba de un estimador, empleando un algoritmo basado en un modelo de regresión multi-salida denominado Random Forest. Este último es capaz de detectar automáticamente el área de la imagen en la cual se encuentra el tejido blando nasal prediciendo simultáneamente los 4 valores que representan los bordes del área de interés mencionada.Sociedad Argentina de Informática e Investigación Operativ