This article depicts the role of the Digital Calibration Certificate (DCC) for an end-to-end digital quality infrastructure and as the basis for developments that are designated by the keyword “Industry 4.0”. Furthermore, it describes the impact the DCC has on increasing productivity in the manufacturing of products and in global trade. The DCC project is international in its scope. Calibration certificates document the measurement capability of a measurement system. They do this independently and by providing traceability to measurement standards. Therefore, they do not only play an important role in the world of metrology, but they also make it possible for manufacturing and commercial enterprises to exchange measurement values reliably and correctly at the national and at the international level. Thus, a DCC concept is urgently needed for the end-to-end digitalization of industry for the era of Industry 4.0 and for Medicine 4.0. A DCC brings about important advantages for issuers and for users. The DCC leads to the stringent, end-to-end, traceable and process-oriented organization of manufacturing and trading. Digitalization is thus a key factor in the field of calibration as it enables significant improvements in product and process quality. The reason for this is that the transmission of errors will be prevented, and consequently, costs will be saved as the time needed for distributing and disseminating the DCCs and the respective calibration objects will be reduced. Furthermore, it will no longer be necessary for the test equipment administration staff to update the data manually, which is a time-consuming, tedious and error-prone process.

This paper addresses digital staining and classification of the unstained white blood cell images obtained with a differential contrast microscope. We have data coming from multiple domains that are partially labeled and partially matching across the domains. Using unstained images removes time-consuming staining procedures and could facilitate and automatize comprehensive diagnostics. To this aim, we propose a method that translates unstained images to realistically looking stained images preserving the inter-cellular structures, crucial for the medical experts to perform classification. We achieve better structure preservation by adding auxiliary tasks of segmentation and direct reconstruction. Segmentation enforces that the network learns to generate correct nucleus and cytoplasm shape, while direct reconstruction enforces reliable translation between the matching images across domains. Besides, we build a robust domain agnostic latent space by injecting the target domain label directly to the generator, i.e., bypassing the encoder. It allows the encoder to extract features independently of the target domain and enables an automated domain invariant classification of the white blood cells. We validated our method on a large dataset composed of leukocytes of 24 patients, achieving state-of-the-art performance on both digital staining and classification tasks.

The field of optical 3D metrology is gaining significant interest in the past years. Optical sensors can probe the geometry of workpieces and biological samples very fast, highly accurate and without any tactile physical contact to the object’s surface. In this respect, optical sensors are a pre-requisite for many applications in the big trends like Industrial Internet of Things, Industry 4.0 or Medicine 4.0. The interest for optical 3D metrology is shifting from a metrology for quality assurance in industrial production to “digitize the real world” to facilitate a precise digital representation of an object or an environment for documentation or as input data for virtual applications like digital fab or augmented reality. The aspiration to digitize the world necessitates fast and efficient contact free sensing principles of appropriate accuracy for solid and even soft objects with a variety of colour, surface texture and lighting conditions. This review article tries to give a concise conceptual overview about the evolution of a broad variety of optical measurement principles that evolved and gained some importance in the field of 3D metrology for industrial 3D applications and their related technological enablers.

The GEMIMEG-II project is intended to pave the way for digitalization in metrology. The central element of this digitalization initiative is the digital calibration certificate (DCC). It contains all calibration information in full digital form. This means, that it is machine readable and machine understandable without human interaction. This enables its utilization by being securely machine interpretable and machine actionable in the entire chain of truly digital workflows and information technology (IT) environments in Industry 4.0. Therefore, the DCC is created automatically in the calibration process in a standardized form based on a digital document schema. This systematic schema enables to safely transfer, process, and interpret all data in the DCC automatically in all subsequent IT based processes. This paper reflects the project status of GEMIMEG-II in its final phase and shares some insights on the concepts developed and solutions implemented as the results will be demonstrated in five Realbeds. Furthermore, the concept of quality of sensing and quality of data will be introduced as it is implemented in the GEMIMEG-II project to convey supplementary information on the measurement, environmental and/or surrounding modalities, and data quality. Finally, a brief outlook will be given on next steps and actions planned in the project related to other digitalization initiatives for the fab of the future.

Chemical staining of the blood smears is one of the crucial components of blood analysis. It is an expensive, lengthy and sensitive process, often prone to produce slight variations in colour and seen structures due to a lack of unified protocols across laboratories. Even though the current developments in deep generative modelling offer an opportunity to replace the chemical process with a digital one, there are specific safety-ensuring requirements due to the severe consequences of mistakes in a medical setting. Therefore digital staining system would profit from an additional confidence estimation quantifying the quality of the digitally stained white blood cell. To this aim, during the staining generation, we disentangle the latent space of the Generative Adversarial Network, obtaining separate representations of the white blood cell and the staining. We estimate the generated image’s confidence of white blood cell structure and staining quality by corrupting these representations with noise and quantifying the information retained between multiple outputs. We show that confidence estimated in this way correlates with image quality measured in terms of LPIPS values calculated for the generated and ground truth stained images. We validate our method by performing digital staining of images captured with a Differential Inference Contrast microscope on a dataset composed of white blood cells of 24 patients. The high absolute value of the correlation between our confidence score and LPIPS demonstrates the effectiveness of our method, opening the possibility of predicting the quality of generated output and ensuring trustworthiness in medical safety-critical setup.