Reducing Manual Annotation Costs for Cell Segmentation by Upgrading Low-Quality Annotations.

Deep-learning algorithms for cell segmentation typically require large data sets with high-quality annotations to be trained with. However, the annotation cost for obtaining such sets may prove to be prohibitively expensive. Our work aims to reduce the time necessary to create high-quality annotations of cell images by using a relatively small well-annotated data set for training a convolutional neural network to upgrade lower-quality annotations, produced at lower annotation costs.

Enabling 3D CT-scanning of cultural heritage objects using only in-house 2D X-ray equipment in museums.

Visualizing the internal structure of museum objects is a crucial step in acquiring knowledge about the origin, state, and composition of cultural heritage artifacts. Among the most powerful techniques for exposing the interior of museum objects is computed tomography (CT), a technique that computationally forms a 3D image using hundreds of radiographs acquired in a full circular range. However, the lack of affordable and versatile CT equipment in museums, combined with the challenge of transporting precious collection objects, currently keeps this technique out of reach for most cultural heritage applications.

Quantifying the effect of X-ray scattering for data generation in real-time defect detection.

Background: X-ray imaging is widely used for the non-destructive detection of defects in industrial products on a conveyor belt. In-line detection requires highly accurate, robust, and fast algorithms. Deep Convolutional Neural Networks (DCNNs) satisfy these requirements when a large amount of labeled data is available.

How auto-differentiation can improve CT workflows: classical algorithms in a modern framework.

Many of the recent successes of deep learning-based approaches have been enabled by a framework of flexible, composable computational blocks with their parameters adjusted through an automatic differentiation mechanism to implement various data processing tasks. In this work, we explore how the same philosophy can be applied to existing "classical" (i.e.

Orchid fruit and root movement analyzed using 2D photographs and a bioinformatics pipeline for processing sequential 3D scans.

Premise: Most studies of the movement of orchid fruits and roots during plant development have focused on morphological observations; however, further genetic analysis is required to understand the molecular mechanisms underlying this phenomenon. A precise tool is required to observe these movements and harvest tissue at the correct position and time for transcriptomics research.

Methods: We utilized three-dimensional (3D) micro-computed tomography (CT) scans to capture the movement of fast-growing roots, and built an integrated bioinformatics pipeline to process 3D images into 3D time-lapse videos.