Automated segmentation of cell organelles in volume electron microscopy using deep learning.

Recent advances in computing power triggered the use of artificial intelligence in image analysis in life sciences. To train these algorithms, a large enough set of certified labeled data is required. The trained neural network is then capable of producing accurate instance segmentation results that will then need to be re-assembled into the original dataset: the entire process requires substantial expertise and time to achieve quantifiable results. To speed-up the process, from cell organelle detection to quantification across electron microscopy modalities, we propose a deep-learning based approach for fast automatic outline segmentation (FAMOUS), that involves organelle detection combined with image morphology, and 3D meshing to automatically segment, visualize and quantify cell organelles within volume electron microscopy datasets. From start to finish, FAMOUS provides full segmentation results within a week on previously unseen datasets. FAMOUS was showcased on a HeLa cell dataset acquired using a focused ion beam scanning electron microscope, and on yeast cells acquired by transmission electron tomography. RESEARCH HIGHLIGHTS: Introducing a rapid, multimodal machine-learning workflow for the automatic segmentation of 3D cell organelles. Successfully applied to a variety of volume electron microscopy datasets and cell lines. Outperforming manual segmentation methods in time and accuracy. Enabling high-throughput quantitative cell biology.

U-Net based vessel segmentation for murine brains with small micro-magnetic resonance imaging reference datasets.

Identification and quantitative segmentation of individual blood vessels in mice visualized with preclinical imaging techniques is a tedious, manual or semiautomated task that can require weeks of reviewing hundreds of levels of individual data sets. Preclinical imaging, such as micro-magnetic resonance imaging (µMRI) can produce tomographic datasets of murine vasculature across length scales and organs, which is of outmost importance to study tumor progression, angiogenesis, or vascular risk factors for diseases such as Alzheimer's. Training a neural network capable of accurate segmentation results requires a sufficiently large amount of labelled data, which takes a long time to compile. Recently, several reasonably automated approaches have emerged in the preclinical context but still require significant manual input and are less accurate than the deep learning approach presented in this paper-quantified by the Dice score. In this work, the implementation of a shallow, three-dimensional U-Net architecture for the segmentation of vessels in murine brains is presented, which is (1) open-source, (2) can be achieved with a small dataset (in this work only 8 µMRI imaging stacks of mouse brains were available), and (3) requires only a small subset of labelled training data. The presented model is evaluated together with two post-processing methodologies using a cross-validation, which results in an average Dice score of 61.34% in its best setup. The results show, that the methodology is able to detect blood vessels faster and more reliably compared to state-of-the-art vesselness filters with an average Dice score of 43.88% for the used dataset.

Lossy Image Compression in a Preclinical Multimodal Imaging Study.

The growing use of multimodal high-resolution volumetric data in pre-clinical studies leads to challenges related to the management and handling of the large amount of these datasets. Contrarily to the clinical context, currently there are no standard guidelines to regulate the use of image compression in pre-clinical contexts as a potential alleviation of this problem. In this work, the authors study the application of lossy image coding to compress high-resolution volumetric biomedical data. The impact of compression on the metrics and interpretation of volumetric data was quantified for a correlated multimodal imaging study to characterize murine tumor vasculature, using volumetric high-resolution episcopic microscopy (HREM), micro-computed tomography (µCT), and micro-magnetic resonance imaging (µMRI). The effects of compression were assessed by measuring task-specific performances of several biomedical experts who interpreted and labeled multiple data volumes compressed at different degrees. We defined trade-offs between data volume reduction and preservation of visual information, which ensured the preservation of relevant vasculature morphology at maximum compression efficiency across scales. Using the Jaccard Index (JI) and the average Hausdorff Distance (HD) after vasculature segmentation, we could demonstrate that, in this study, compression that yields to a 256-fold reduction of the data size allowed to keep the error induced by compression below the inter-observer variability, with minimal impact on the assessment of the tumor vasculature across scales.

Digital dissection of the head of the frogs Calyptocephalella gayi and Leptodactylus pentadactylus with emphasis on the feeding apparatus.

Micro-computed tomography (microCT) of small animals has led to a more detailed and more accurate three-dimensional (3D) view on different anatomical structures in the last years. Here, we present the cranial anatomy of two frog species providing descriptions of bone structures and soft tissues of the feeding apparatus with comments to possible relations to habitat and feeding ecology. Calyptocephalella gayi, known for its aquatic lifestyle, is not restricted to aquatic feeding but also feeds terrestrially using lingual prehension. This called for a detailed investigation of the morphology of its feeding apparatus and a comparison to a fully terrestrial species that is known to feed by lingual prehension such as Leptodactylus pentadactylus. These two frog species are of similar size, feed on similar diet but within different main habitats. MicroCT scans of both species were conducted in order to reconstruct the complete anatomical condition of the whole feeding apparatus for the first time. Differences in this regard are evident in the tongue musculature, which in L. pentadactylus is more massively built and with a broader interdigitating area of the two main muscles, the protractor musculus genioglossus and the retractor musculus hyoglossus. In contrast, the hyoid retractor (m. sternohyoideus) is more massive in the aquatic species C. gayi. Moreover, due to the different skull morphology, the origins of two of the five musculi adductores vary between the species. This study brings new insights into the relation of the anatomy of the feeding apparatus to the preferred feeding method via 3D imaging techniques. Contrary to the terrestrially feeding L. pentadactylus, the skeletal and muscular adaptations of the aquatic species C. gayi provide a clear picture of necessities prescribed by the habitat. Nevertheless, by keeping a certain amount of flexibility of the design of its feeding apparatus, C. gayi is able to employ various methods of feeding.

The tracheal system of scutigeromorph centipedes and the evolution of respiratory systems of myriapods.

The tracheal system of scutigeromorph centipedes (Chilopoda) is special, as it consists of dorsally arranged unpaired spiracles. In this study, we investigate the tracheal systems of five different scutigeromorph species. They are strikingly similar to each other but depict unique characters compared to the tracheal systems of pleurostigmophoran centipedes, which has engendered an ongoing debate over a single versus independent origin of tracheal systems in Chilopoda. Up to now, only the respiratory system of Scutigera coleoptrata was investigated intensively using LM-, TEM-, and SEM-techniques. We supplement this with data for species from all three families of Scutigeromorpha. These reveal interspecific differences in atrial width and the shape and branching pattern of the tracheal tubules. Further, we investigated the tracheal system of Scutigera coleoptrata with three additional techniques: light sheet microscopy, microCT and synchrotron radiation based microCT analysis. This set of techniques allows a comparison between fresh versus fixed and dried material. The question of a unique vs. multiple origin of tracheal systems in centipedes and in Myriapoda as a whole is discussed with regard to their structural similarities and differences and the presence of hemocyanin as an oxygen carrier. We used morphological and molecular data and the fossil record to evaluate the alternative hypotheses.