Sartorius Transforms Cell Imagery into Data with CloudFactory's Annotation Solution
Company Size
1,000+
Region
- Europe
Country
- Germany
Product
- IncucyteⓇ Live-Cell Analysis System
- Dataloop
- CloudFactory’s Data Annotation Solution
Tech Stack
- Machine Learning
- Artificial Intelligence
- Data Annotation
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Customer Satisfaction
- Innovation Output
- Productivity Improvements
Technology Category
- Analytics & Modeling - Machine Learning
- Analytics & Modeling - Predictive Analytics
- Application Infrastructure & Middleware - Data Visualization
Applicable Industries
- Healthcare & Hospitals
Applicable Functions
- Product Research & Development
- Quality Assurance
Use Cases
- Machine Condition Monitoring
- Predictive Maintenance
- Remote Asset Management
Services
- Data Science Services
- System Integration
- Training
About The Customer
Sartorius is a biotechnology company that supports scientists and engineers in developing and manufacturing medications for incurable diseases. By simplifying workflows, enabling faster research, and reducing human error, Sartorius helps the biopharmaceutical industry bring improved medications to patients quickly and safely. One of their key products is the IncucyteⓇ Live-Cell Analysis System, which quantifies cell behavior and captures cellular changes using high-definition phase and fluorescence images. This system extracts and records image data in real-time, allowing scientists to observe complex biological changes as they happen. It also employs machine learning and artificial intelligence to help customers extract more information from samples in simple, easy ways.
The Challenge
The IncucyteⓇ Live-Cell Analysis System automates live-cell imaging, producing a vast number of microscopic images that are beyond human capacity to analyze. Neural networks can handle this task, but they require a large, annotated dataset for training. Sartorius needed to identify and characterize individual cells within these images, which contain hundreds to millions of cells in various shapes and sizes. Although the IncucyteⓇ system has integrated software for automatic cell segmentation, it requires user training and bespoke analysis for different cell types. The neural network method, however, allows for a single analysis across a wide range of cell types without user training. Faced with the diversity and volume of cells needed for the training dataset, Sartorius realized they couldn't manage the annotation process alone.
The Solution
Rickard Sjögren, a senior scientist at Sartorius, led the initiative to find a solution for the data annotation problem. After comparing several vendors, Sartorius chose to partner with CloudFactory for three main reasons: low turnover and a dedicated team of data analysts, matching values in terms of ethical workforce practices, and the combination of CloudFactory’s managed workforce with the Dataloop platform. CloudFactory’s data analysts use Dataloop to perform individual cell segmentation and identify key cell types among thousands in each image. Nicola Bevan, Manager of Cell Imaging Applications, highlighted that the combination of workforce and platform contributes to project success. CloudFactory’s trained annotators apply precision labeling techniques to complex microscopy images, even when cells are touching, out of focus, or overlapping. They also support cell tracking projects by drawing bounding boxes around cells as they move between frames in time-lapse datasets.
Operational Impact
Quantitative Benefit
Case Study missing?
Start adding your own!
Register with your work email and create a new case study profile for your business.
Related Case Studies.
Case Study
Hospital Inventory Management
The hospital supply chain team is responsible for ensuring that the right medical supplies are readily available to clinicians when and where needed, and to do so in the most efficient manner possible. However, many of the systems and processes in use at the cancer center for supply chain management were not best suited to support these goals. Barcoding technology, a commonly used method for inventory management of medical supplies, is labor intensive, time consuming, does not provide real-time visibility into inventory levels and can be prone to error. Consequently, the lack of accurate and real-time visibility into inventory levels across multiple supply rooms in multiple hospital facilities creates additional inefficiency in the system causing over-ordering, hoarding, and wasted supplies. Other sources of waste and cost were also identified as candidates for improvement. Existing systems and processes did not provide adequate security for high-cost inventory within the hospital, which was another driver of cost. A lack of visibility into expiration dates for supplies resulted in supplies being wasted due to past expiry dates. Storage of supplies was also a key consideration given the location of the cancer center’s facilities in a dense urban setting, where space is always at a premium. In order to address the challenges outlined above, the hospital sought a solution that would provide real-time inventory information with high levels of accuracy, reduce the level of manual effort required and enable data driven decision making to ensure that the right supplies were readily available to clinicians in the right location at the right time.
Case Study
Gas Pipeline Monitoring System for Hospitals
This system integrator focuses on providing centralized gas pipeline monitoring systems for hospitals. The service they provide makes it possible for hospitals to reduce both maintenance and labor costs. Since hospitals may not have an existing network suitable for this type of system, GPRS communication provides an easy and ready-to-use solution for remote, distributed monitoring systems System Requirements - GPRS communication - Seamless connection with SCADA software - Simple, front-end control capability - Expandable I/O channels - Combine AI, DI, and DO channels
Case Study
Driving Digital Transformations for Vitro Diagnostic Medical Devices
Diagnostic devices play a vital role in helping to improve healthcare delivery. In fact, an estimated 60 percent of the world’s medical decisions are made with support from in vitrodiagnostics (IVD) solutions, such as those provided by Roche Diagnostics, an industry leader. As the demand for medical diagnostic services grows rapidly in hospitals and clinics across China, so does the market for IVD solutions. In addition, the typically high cost of these diagnostic devices means that comprehensive post-sales services are needed. Wanteed to improve three portions of thr IVD:1. Remotely monitor and manage IVD devices as fixed assets.2. Optimizing device availability with predictive maintenance.3. Recommending the best IVD solution for a customer’s needs.
Case Study
HaemoCloud Global Blood Management System
1) Deliver a connected digital product system to protect and increase the differentiated value of Haemonetics blood and plasma solutions. 2) Improve patient outcomes by increasing the efficiency of blood supply flows. 3) Navigate and satisfy a complex web of global regulatory compliance requirements. 4) Reduce costly and labor-intensive maintenance procedures.
Case Study
Harnessing real-time data to give a holistic picture of patient health
Every day, vast quantities of data are collected about patients as they pass through health service organizations—from operational data such as treatment history and medications to physiological data captured by medical devices. The insights hidden within this treasure trove of data can be used to support more personalized treatments, more accurate diagnosis and more advanced preparative care. But since the information is generated faster than most organizations can consume it, unlocking the power of this big data can be a struggle. This type of predictive approach not only improves patient care—it also helps to reduce costs, because in the healthcare industry, prevention is almost always more cost-effective than treatment. However, collecting, analyzing and presenting these data-streams in a way that clinicians can easily understand can pose a significant technical challenge.
