The field of cell biology is undergoing rapid advancement, necessitating a comprehensive comprehension of the intricate dynamics and heterogeneity exhibited by individual cells in order to unravel complex cellular processes. Time-lapse microscopy has emerged as an influential methodology, enabling the investigation of cellular behavior at the single-cell scale with exceptional temporal resolution. Nonetheless, the analysis of the extensive data produced by time-lapse microscopy poses a substantial obstacle. DeepSea is an innovative deep-learning model and software tool designed to streamline the process of segmenting and tracking individual cells in time-lapse microscopy images. A recent publication in Cell Reports Methods describes DeepSea as one of the most accurate tools of its sort.

Accurate delineation and monitoring of individual cells over time play a vital role in quantitative analysis. Understanding the dynamic nature of cells presents a formidable challenge as their behavior constantly changes. Fortunately, recent progress in deep learning provides a ray of hope for automating the analysis of microscopy images.

DeepSea: A Versatile and Trainable Deep Learning Model

Deep learning models have shown remarkable effectiveness in a range of image processing duties, encompassing the precise separation and tracking of objects. In the realm of cell biology and microscopy, they have garnered considerable attention. DL-driven segmentation methods have notably enhanced the ability to identify cell bodies within microscopy images. DeepSea takes advantage of these advancements by combining segmentation and tracking models into an integrated platform, offering a comprehensive solution for the examination of time-lapse microscopy data.

The program seamlessly integrates cell segmentation and tracking, presenting a user-friendly interface. The model’s enhanced efficiency and reduced parameter complexity make it highly effective. Furthermore, the software’s capability to train the model for specific cell types of interest will greatly expedite future discoveries.

Time-lapse microscopy, an imaging technique that captures a series of microscope images over a period, grants researchers the ability to observe and analyze dynamic cellular processes. By tracking the progression of events at the individual cell level, such as cellular differentiation and morphological changes, scientists gain insights into the intricate dynamics of biological phenomena. This approach holds great potential for uncovering novel discoveries in cellular biology.

After acquiring the images, scientists are confronted with two primary tasks: segmentation, which involves discerning the boundaries of individual cells amidst the background and neighboring cells, and tracking, which entails tracing the movement of a cell across consecutive frames. Subsequently, researchers can delve into various aspects such as size, morphology, texture, dynamic behavior, shape transformations, and other relevant attributes.

Analyzing microscopy images manually is a time-consuming task that is better suited for automation, and this is where DeepSea excels. The efficient deep learning model can swiftly distinguish cells in under a second and accurately track them with a remarkable 98% precision.

Enabling the software to recognize cell division proved to be an exceptional and challenging aspect of this endeavor. It is a rare scenario where artificial intelligence and computer vision track the process of one entity splitting into two distinct entities.

DeepSea possesses the capability to track a wide range of cell types due to its versatility as a generalizable model. It utilizes a simplified variant of the popular 2D-UNET model, significantly reducing the number of parameters while ensuring a combination of exceptional precision and rapid processing speeds.

At present, the model demonstrates superior outcomes in terms of accuracy and efficiency, particularly for specific cell types, when compared to leading models for cell segmentation.

Due to the challenge of distinguishing cell bodies from their backgrounds in low-contrast images, the scientists took the initiative to manually segment a set of cell images, which served as the training data for DeepSea. The researchers also created a software application, which is also accessible at DeepSeas.org, to assist in cropping, labeling, and modifying the microscope images of cells.

DeepSea achieves remarkable precision when applied to various types of cells, thanks to the inclusion of lung, muscle, and stem cell images in its training dataset. To further enhance the model’s capabilities, future iterations could incorporate a broader range of cell types.

Performance Evaluation and Application of DeepSea 

DeepSea’s performance is evaluated against existing state-of-the-art segmentation and tracking models using standard metrics such as intersection over union (IoU) and average precision (AP). The results demonstrate that DeepSea outperforms other models in terms of precision, particularly in challenging scenarios with densely packed cells and touching cell edges. The segmentation and tracking capabilities of DeepSea are shown to be robust across different cell densities and types.

To showcase the practical application of DeepSea, the researchers utilized it to investigate the size regulation of embryonic stem cells, which are the cornerstone of multicellular life and can develop into any other cell type. Scientists discovered that embryonic stem cells, known to proliferate extremely quickly, control their size so that smaller cells spend more time developing before creating the next generation of cells.

The researchers made a fascinating discovery regarding embryonic stem cells; when these cells are initially small in size, they exhibit an awareness of their own dimensions and subsequently undergo an extended period of development before undergoing further division. The precise reasons and mechanisms behind this phenomenon remain unknown; however, the existence of such behavior in stem cells is noteworthy.

The scientists plan to utilize their existing software in upcoming studies to gather information regarding the spatial relationships among cells and how cellular characteristics assemble into 3D configurations, thereby constructing complex structures.

The scientists are working towards overcoming limitations encountered while utilizing their deep learning models. One particular challenge they face is the scarcity of labeled cell images required for training these models. They look forward to applying Generative Adversarial Networks (GANs), a machine learning framework, to address this challenge. Using GANs, they can generate synthetic data, specifically annotated images of cells. This approach aims to reduce the time and effort required for manual labeling. Consequently, the researchers will have access to extensive datasets encompassing various cell types, all achieved with minimal human intervention.

Conclusion

DeepSea represents a significant advancement in single-cell time-lapse microscopy by providing a versatile and trainable DL-based model for automated segmentation and tracking of individual cells. The user-friendly software tool enables researchers to analyze large datasets of live microscopy images with precision and efficiency.

Article Source: Reference Paper | Reference Article | DeepSea’s model training dataset, software, and open-source code are available at: Website

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Dr. Tamanna Anwar is a Scientist and Co-founder of the Centre of Bioinformatics Research and Technology (CBIRT). She is a passionate bioinformatics scientist and a visionary entrepreneur. Dr. Tamanna has worked as a Young Scientist at Jawaharlal Nehru University, New Delhi. She has also worked as a Postdoctoral Fellow at the University of Saskatchewan, Canada. She has several scientific research publications in high-impact research journals. Her latest endeavor is the development of a platform that acts as a one-stop solution for all bioinformatics related information as well as developing a bioinformatics news portal to report cutting-edge bioinformatics breakthroughs.

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