Classification of Jaw Bone Cysts and Necrosis via the Processing of Orthopantomograms

The authors analyze the design of a method for automatized evaluation of parameters in orthopantomographic images capturing pathological tissues developed in human jaw bones. The main problem affecting the applied medical diagnostic procedures consists in low repeatability of the performed evaluation. This condition is caused by two aspects, namely subjective approach of the involved medical specialists and the related exclusion of image processing instruments from the evaluation scheme. The paper contains a description of the utilized database containing images of cystic jaw bones; this description is further complemented with appropriate schematic repre¬sentation. Moreover, the authors present the results of fast automatized segmentation realized via the live-wire method and compare the obtained data with the results provided by other segmentation techniques. The shape parameters and the basic statistical quantities related to the distribution of intensities in the segmented areas are selected. The evaluation results are provided in the final section of the study; the authors correlate these values with the subjective assessment carried out by radiologists. Interestingly, the paper also comprises a discussion presenting the possibility of using selected parameters or their combinations to execute automatic classification of cysts and osteonecrosis. In this context, a comparison of various classifiers is performed, including the Decision Tree, Naive Bayes, Neural Network, k-NN, SVM, and LDA classifica¬tion tools. Within this comparison, the highest degree of accuracy (85% on the average) can be attributed to the Decision Tree, Naive Bayes, and Neural Network classifier

Segmentación automática de procesos neuronales en microscopı́a electrónica mediante técnicas de aprendizaje profundo

En este trabajo se han utilizado redes neuronales convolucionales para la seg- mentación de imágenes biomédicas obtenidas mediante microscopia electrónica. El trabajo se ha desarrollado usando la librerı́a de Keras y ayudándonos de la herramienta Google Colaboratory para la ejecución de los modelos más pesados computacionalmente. Se ha comenzado entrenando redes neuronales artificiales para ir adentrándonos en el funcionamiento de la librerı́a. Después se ha entrenado una red preentrenada, concretamente la VGG16, bloqueando todas sus capas convolucionales y dejando alguna desbloqueada. Y finalmente se ha modelado una red neuronal convolucional siguiendo la estructura de la red U-Net. Esta red ha dado buenos resultados en la segmentación de imágenes y se utiliza sobre todo en la segmentación de imágenes biomédicas. La base de datos del caso principal, se ha obtenido de la competición lanza- da en el International Symposium on Biomedical Imaging (ISBI) de 2012 y está compuesta por un conjunto de cortes de microscopia electrónica para entrenar algoritmos de aprendizaje automáticos y ası́ poder realizar la segmentación au- tomática de neuritas