Extracting morphologies from third harmonic generation images of structurally normal human brain tissue

Marine ship instance segmentation by deep neural networks using a global and local attention (GALA) mechanism

Marine ships are the transport vehicle in the ocean and instance segmentation of marine ships is an accurate and efficient analysis approach to achieve a quantitative understanding of marine ships, for example, their relative locations to other ships or obstacles. This relative spatial information is crucial for developing unmanned ships to avoid crashing. Visible light imaging, e.g. using our smartphones, is an efficient way to obtain images of marine ships, however, so far there is a lack of suitable open-source visible light datasets of marine ships, which could potentially slow down the development of unmanned ships. To address the problem of insufficient datasets, here we built two instance segmentation visible light datasets of marine ships, MariBoats and MariBoatsSubclass, which could facilitate the current research on instance segmentation of marine ships. Moreover, we applied several existing instance segmentation algorithms based on neural networks to analyze our datasets, but their performances were not satisfactory. To improve the segmentation performance of the existing models on our datasets, we proposed a global and local attention mechanism for neural network models to retain both the global location and semantic information of marine ships, resulting in an average segmentation improvement by 4.3% in terms of mean average precision. Therefore, the presented new datasets and the new attention mechanism will greatly advance the marine ship relevant research and applications.

Compact portable multiphoton microscopy reveals histopathological hallmarks of unprocessed lung tumor tissue in real time

During lung cancer operations a rapid and reliable assessment of tumor tissue can reduce operation time and potentially improve patient outcomes. We show that third harmonic generation (THG), second harmonic generation (SHG) and two-photon excited autofluorescence (2PEF) microscopy reveals relevant, histopathological information within seconds in fresh unprocessed human lung samples. We used a compact, portable microscope and recorded images within 1 to 3 seconds using a power of 5 mW. The generated THG/SHG/2PEF images of tumorous and nontumorous tissues are compared with the corresponding standard histology images, to identify alveolar structures and histopathological hallmarks. Cellular structures (tumor cells, macrophages and lymphocytes) (THG), collagen (SHG) and elastin (2PEF) are differentiated and allowed for rapid identification of carcinoid with solid growth pattern, minimally enlarged monomorphic cell nuclei with salt-and-pepper chromatin pattern, and adenocarcinoma with lipidic and micropapillary growth patterns. THG/SHG/2PEF imaging is thus a promising tool for clinical intraoperative assessment of lung tumor tissue.

Automated interpretation of time-lapse quantitative phase image by machine learning to study cellular dynamics during epithelial-mesenchymal transition

SIGNIFICANCE

Machine learning is increasingly being applied to the classification of microscopic data. In order to detect some complex and dynamic cellular processes, time-resolved live-cell imaging might be necessary. Incorporating the temporal information into the classification process may allow for a better and more specific classification.

AIM

We propose a methodology for cell classification based on the time-lapse quantitative phase images (QPIs) gained by digital holographic microscopy (DHM) with the goal of increasing performance of classification of dynamic cellular processes.

APPROACH

The methodology was demonstrated by studying epithelial-mesenchymal transition (EMT) which entails major and distinct time-dependent morphological changes. The time-lapse QPIs of EMT were obtained over a 48-h period and specific novel features representing the dynamic cell behavior were extracted. The two distinct end-state phenotypes were classified by several supervised machine learning algorithms and the results were compared with the classification performed on single-time-point images.

RESULTS

In comparison to the single-time-point approach, our data suggest the incorporation of temporal information into the classification of cell phenotypes during EMT improves performance by nearly 9% in terms of accuracy, and further indicate the potential of DHM to monitor cellular morphological changes.

CONCLUSIONS

Proposed approach based on the time-lapse images gained by DHM could improve the monitoring of live cell behavior in an automated fashion and could be further developed into a tool for high-throughput automated analysis of unique cell behavior.