Segmentation metric misinterpretations in bioimage analysis.

Quantitative evaluation of image segmentation algorithms is crucial in the field of bioimage analysis. The most common assessment scores, however, are often misinterpreted and multiple definitions coexist with the same name. Here we present the ambiguities of evaluation metrics for segmentation algorithms and show how these misinterpretations can alter leaderboards of influential competitions. We also propose guidelines for how the currently existing problems could be tackled.

Cell segmentation and representation with shape priors.

Cell segmentation is a fundamental problem of computational biology, for which convolutional neural networks yield the best results nowadays. This field is expanding rapidly, and in the recent years, shape-constrained segmentation models emerged as strong competitors to traditional, pixel-based segmentation methods for instance segmentation. These methods predict the parameters of the underlying shape model, so choosing the right shape representation is critical for the success of the segmentation. In this study, we introduce two new representation-based deep learning segmentation methods after a quantitative comparison of the most important shape descriptors in the literature. Our networks are based on Fourier coefficients and statistical shape models, both of which have proven to be reliable tools for cell shape modelling. Our results indicate that the methods are competitive alternatives to the most widely used baseline deep learning algorithms, especially when the number of parameters for the underlying shape model are low or the cells to be segmented have irregular morphologies.

Radioulnar interphalangeal joint angles in children and adolescents aged 0 to 19 years.

The purpose of this study is to determine the normal ranges of radioulnar (i.e. medial-lateral) finger deviations during growth. We retrospectively measured radioulnar interphalangeal joint angles in 6236 properly aligned thumbs and fingers in trauma radiographs of 4720 patients aged 0 to 19 years. The mean interphalangeal joint angle of the thumb was 0.2° (standard deviation 1.5°). The average proximal interphalangeal joint angles were ulnar deviation of 2.5° (1.7°) for the index, ulnar deviation 1.7° (1.5°) for the middle, radial deviation 1.3° (1.8°) for the ring, radial deviation 2.0° (2.8°) for the little fingers. The distal interphalangeal joint angles were ulnar deviation of 2.5° (1.7°), ulnar deviation 2.1° (1.7°), radial deviation 2.1° (1.7°), radial deviation 5.1° (2.8°) from index to the little fingers. Thumbs were typically straight, whereas the index and middle fingers deviated ulnarly, and ring and little fingers radially. There were no relevant differences in sex or laterality.

Cell lines and clearing approaches: a single-cell level 3D light-sheet fluorescence microscopy dataset of multicellular spheroids.

Nowadays, three dimensional (3D) cell cultures are widely used in the biological laboratories and several optical clearing approaches have been proposed to visualize individual cells in the deepest layers of cancer multicellular spheroids. However, defining the most appropriate clearing approach for the different cell lines is an open issue due to the lack of a gold standard quantitative metric. In this article, we describe and share a single-cell resolution 3D image dataset of human carcinoma spheroids imaged using a light-sheet fluorescence microscope. The dataset contains 90 multicellular cancer spheroids derived from 3 cell lines (i.e. T-47D, 5-8F, and Huh-7D12) and cleared with 5 different protocols, precisely ClearT, ClearT2, CUBIC, ScaleA2, and Sucrose. To evaluate image quality and light penetration depth of the cleared 3D samples, all the spheroids have been imaged under the same experimental conditions, labelling the nuclei with the DRAQ5 stain and using a Leica SP8 Digital LightSheet microscope. The clearing quality of this dataset was annotated by 10 independent experts and thus allows microscopy users to qualitatively compare the effects of different optical clearing protocols on different cell lines. It is also an optimal testbed to quantitatively assess different computational metrics evaluating the image quality in the deepest layers of the spheroids.

A quantitative metric for the comparative evaluation of optical clearing protocols for 3D multicellular spheroids.

3D multicellular spheroids quickly emerged as in vitro models because they represent the in vivo tumor environment better than standard 2D cell cultures. However, with current microscopy technologies, it is difficult to visualize individual cells in the deeper layers of 3D samples mainly because of limited light penetration and scattering. To overcome this problem several optical clearing methods have been proposed but defining the most appropriate clearing approach is an open issue due to the lack of a gold standard metric. Here, we propose a guideline for 3D light microscopy imaging to achieve single-cell resolution. The guideline includes a validation experiment focusing on five optical clearing protocols. We review and compare seven quality metrics which quantitatively characterize the imaging quality of spheroids. As a test environment, we have created and shared a large 3D dataset including approximately hundred fluorescently stained and optically cleared spheroids. Based on the results we introduce the use of a novel quality metric as a promising method to serve as a gold standard, applicable to compare optical clearing protocols, and decide on the most suitable one for a particular experiment.