ABSTRACT
The size of the cerebellum in ultrasound volumes of the fetal brain has shown a high correlation with gestational age, which makes it a valuable feature to detect fetal growth restrictions. Manual annotation of the 3D surface of the cerebellum in an ultrasound volume is a time consuming task, which needs to be performed by a highly trained expert. In order to assist the experts in the evaluation of cerebellar dimensions, we developed an automatic scheme for the segmentation of the 3D surface of the cerebellum in ultrasound volumes, using a spherical harmonics model. In this work we present our validation results on 10 ultrasound volumes in which we have obtained an adequate accuracy in the segmentation of the cerebellum (mean Dice coefficient of 0.689). The method reported shows potential to effectively assist the experts in the assessment of fetal growth in ultrasound volumes.
El tamaño del cerebelo, en un volumen de ultrasonido del cerebro fetal, ha mostrado una alta correlación con la edad gestacional, lo que hace importante a esta medición para la detección de restricciones del crecimiento del feto. La anotación manual de la superficie 3D del cerebelo en un volumen de ultrasonido es una tarea demandante, que debe ser realizada por un experto. Con el propósito de apoyar a los expertos en la evaluación de las dimensiones del cerebelo fetal, hemos desarrollado un método automático para la segmentación de la superficie 3D del cerebelo en volúmenes de ultrasonido, utilizando un modelo de harmónicos esféricos (spherical harmonics). En este trabajo presentamos los resultados de una evaluación del método automático en 10 volúmenes de ultrasonido con los que hemos obtenido un valor adecuado de exactitud (coeficiente promedio de Dice de 0.689). El método reportado tiene potencial para asistir de manera efectiva a los expertos en la evaluación del crecimiento fetal, utilizando volúmenes de ultrasonido.
ABSTRACT
The mitotic index (MI) is an important measure in cell proliferation studies. Determination of the MI is usually made by light-microscope analysis of slide preparations. The analyst identifies and counts thousands of cells and reports the percentage of mitotic shapes found among the interphase nuclei. Full automation of this process is an ambitious task, because there can exist very few mitotic shapes among hundreds of nuclei and thousands of artifacts, resulting in a high probability of false positives, i.e. objects erroneously identified as mitosis or nuclei. A semi-automated approach for MI calculation is reported, based on the development of a neural network (NN) for automatic identification of metaphase spreads and stimulated nuclei in digital images of microscope preparations at 10X magnification. After segmentation of the objects on each image, ten different morphometrical, photometrical and textural features are measured on each segmented object. An NN is used to classify the feature vectors into three classes: metaphases, nuclei and artifacts. The system has been able to classify correctly approximately 91% of the objects in each class, in a test set of 191 mitosis, 331 nuclei and 387 artifacts, obtained from 30 different microscope slides. Manual editing of false positives from the metaphase classification results allows the calculation of the MI with an error of 6.5%.