Публикации

В данной статье получены вычислимые оценки скорости сходимости нормированной статистики Пиллая к стандартному нормальному распределению при условии, что размерность данных возрастает пропорционально объёму выборки. Приведённый результат позволяет корректно вычислять p-значения в прикладных задачах многомерного анализа данных. Задачи в постановке, когда число анализируемых признаков сравнимо с объемом выборки, все чаще возникают в области обработки сигналов.

AnnotatorJ combines single-cell identification with deep learning and manual annotation. Cellular analysis quality depends on accurate and reliable detection and segmentation of cells so that the subsequent steps of analyses e.g. expression measurements may be carried out precisely and without bias. Deep learning has recently become a popular way of segmenting cells, performing unimaginably better than conventional methods. However, such deep learning applications may be trained on a large amount of annotated data to be able to match the highest expectations. High-quality annotations are unfortunately expensive as they require field experts to create them, and often cannot be shared outside the lab due to medical regulations.

We propose AnnotatorJ, an ImageJ plugin for the semi-automatic annotation of cells (or generally, objects of interest) on (not only) microscopy images in 2D that helps find the true contour of individual objects by applying U-Net-based pre-segmentation. The manual labour of hand-annotating cells can be significantly accelerated by using our tool. Thus, it enables users to create such datasets that could potentially increase the accuracy of state-of-the-art solutions, deep learning or otherwise, when used as training data.