Final stitched volume
A representative axial slice from the final stitched volume of the lung data is shown in Fig. 4. The left panel shows an overview over the chest region containing the lung. A closer view of the small orange square is shown on the right panel. An animated view and progressive zoom-in into this slice image can be seen in the Electronic Supplementary Material, ESM 1.
In Fig. 5, a volume renderingFootnote 2 of bones and air spaces is shown. The grey box marks the edges of the scanned volume with the lines indicating positions of individual wide-field scans. The same volume is rendered from different perspectives in the movie ESM 2 in the Electronic Supplementary Material.
One of the first steps of analyzing and quantifying the data is segmentation. During this process, labels are assigned to parts of data corresponding to different objects, materials, etc. The segmentation process depends on the grey-level difference as well as noise levels. Thanks to the superior performance of the new optics used during the experiment (Bührer et al. 2019), the final stitched dataset shows great image quality with high contrast between the air, tissue, and bone regions (see Fig. 4), such that a simple iso-threshold technique (Velasco 1980) allows for a clear separation of the different components. Additional post-processing is still required to eliminate false detection and produce a reliable segmentation of the volume. Below, the individual steps for obtaining the segmentation of the final stitched reduced volume of the lung region are described.
First, pixels corresponding to the bone regions were masked out. These regions showed significantly higher grey-level values in comparison with the other scan regions and were easily rejected with a single global threshold.
Subsequently, for each slice, iso-threshold values were found for the remaining pixels to separate them into two subsets, namely air and tissue, and extract airway regions. A cylindrical mask, roughly limiting the position of the lung, was used to reject pixels at the image edges (which were located outside the animal) with grey-level values similar to those in the airway system. However, after this step the segmentation still contained non-air regions assigned to the airways and thus required further cleaning steps.
In the next step, non-air pixels were removed from the segmentation volume by selecting only 3D connected pixels of the lung airways. The processing was done using the pi2 software package.Footnote 3 The pi2 library distributes the image analysis tasks to a computer cluster automatically without user intervention.
Lastly, a second iteration of the iso-threshold operation was done. The threshold values were found again for each slice using only pixels selected by a dilated segmentation from the previous step. Dilation ensured that only air and adjacent tissue pixels were used for segmentation. Additionally, a sharpness filter was applied to increase the contrast difference at the edges of the air spaces.
The output of the segmentation process is a binary image with the same size as the stitched image, where slices contain the label mask of the airways in the lung. An example of a segmented region is shown in Fig. 6. This mask is useful for further quantitative analysis of lung structure and morphology. A straightforward pixel count and subsequent conversion to physical units of length provides an estimate of the overall volume of the airway system of approximately 1.1 cm3.
In earlier studies (Lovric et al. 2017b; Oikonomidis et al. 2017a), a different and more complex approach was used to segment images with an isotropic pixel size of 1.1 × 1.1 µm2 to precisely identify alveolar septa. Since at the present imaging resolution, namely at pixel sizes of 2.75 × 2.75 µm2, alveolar septa are slightly below the resolution limit, the above described steps already yielded sufficiently good results for many quantitative analyses. However, depending on the exact nature of the analysis to be performed, more sophisticated segmentation procedures, potentially making use of prior knowledge about the sample’s structure, may have to be employed.
This work presents a micrometer-resolution tomographic dataset of a full intact juvenile rat lung. While it mainly focuses on technical details of acquisition and post-processing steps, some potential applications of this dataset for further studies are discussed in the following.
Imaging intact biological tissue is a demanding task, which puts strict technical requirements on the experimental setup. For standard X-ray tomographic imaging, various methods exist to prepare excised lungs to prevent its degradation and motion during the slow acquisition process. However, not many details are known about how much the structure of the lung is altered during the preparation process. For example, an intratracheal instillation of fixative changes the surface tension dramatically. It has to be expected that the fine structure is modified due to the alteration of the surface tension. Critical point drying causes a shrinkage of 50–60% and paraffin embedding 30–40%. Due to different physical properties of different structures, e.g. cellular versus extracellular matrix, a regular shrinkage may not be assumed. At the same token, it is not predictable if the shrinkage happens regularly or at which extent and where it is irregular. Therefore, it is important to have a high-resolution tomographic dataset of intact tissue which represents real lungs better than any dataset obtained from excised lungs.
Concerning morphological properties of the lung, still little is known about the size distribution of the pulmonary acini and alveoli. It remains a question of debate how much the volume of the acini and alveoli varies across the entire lung and if there are regions with larger or smaller variations. Further statistical analysis of the whole lung can bring a quantitative answer to this question. At the same time, it can validate the approach of previous studies of acinar properties, where only a part of the lung was scanned (Haberthür et al. 2013), by giving a number on how large this volume needs to be and which regions are the best to target. Our dataset allows such studies to be done from macro- to micro- scale on the full lung volume.
The dataset can also provide a deeper understanding of biological processes such as gas exchange and particle deposition in the lung through a quantitative characterization of the size and complexity of acini in different regions of the lung. The individual sizes and local numbers of these structural units are responsible for the absorption of particles from the air. The larger the acini are, the more efficient the deposition is. Furthermore, the deposition appears to be higher at the entrance of the acini than in the peripheral regions. From a practical point of view, this information can be used, for example, to predict the degradation of healthy lung tissue in polluted environments or to estimate the amount of medication that needs to be administered in a lung treatment. However, for both, environmental particles or drug application, the local concentrations are much more important than the average.
It is known based on studies of fixed, dried lungs that the acini in contact with the pleura (surface of the lung) are larger than all of the other acini (Kizhakke Puliyakote et al. 2016). However, even if the size of the acini is critical for ventilation and particle deposition, the size distribution of the acini in the entire lung is unknown.
Apart from morphological studies, the fast large-volume tomographic approach opens up a possibility to study, for example, the full lung development in rodents at different age stages. It has been previously shown that the sensitivity of tissue changes with age, but solid quantitative confirmation of these processes is still missing. Furthermore, this method can be used for in-detail studies of the regional microscopic effects of pharmacological and other interventions in models of lung diseases such as asthma, chronic obstructive pulmonary disease, and lung fibrosis.
The acquired dataset can be also interesting as a realistic structure animal model input for simulations to study different internal processes in the lung. The small pixel size of the dataset provides more precise surface area properties and resolves small variations in shapes, which presumably can have a large impact on, for example, gas flow.