Scientific and Technical Goals
About 480,000 people per year in Germany suffer from cancer, which makes this disease the second highest cause of death. Therefore, the improvement of diagnostic and therapeutic options is an important task of cancer research. At TranslaTUM, scientists from the fields of medicine, engineering and natural sciences are working together to improve the chances of cancer patients being cured. Hence, the overarching idea of this project is to quickly transfer new findings from research into clinical practice, a process known as translation.
Various diseases leave traces on cells, in particular blood cells, in different ways. Modern haematology analysers should therefore be able to detect a large number of abnormalities and changes in the morphological structure of the cells and their composition of the cells or blood. Since the observed irregularities can sometimes be very rare, the cells have to be examined in high throughput in order to obtain a statistical result. Such a system is also expected to be autonomous, especially for use in remote areas where the so-called neglected or poverty-related diseases still occur. The duration of the analysis should be short not only in urgent cases, so that the process can be kept as simple as possible for the physician and the patient.
Even if a system is able to provide mentioned services, it must still fulfill at least three additional criteria in order to achieve clinical relevance. First and foremost, it must meet high safety and quality standards. In addition, the used approaches must be explainable, since black box solutions are largely unacceptable in the treatment of humans and animals. Last but not least, the user-friendliness of the proposed programs and devices plays a decisive role in ensuring that they can be used productively. Ultimately, technological progress is aimed at an end-to-end approach that allows a single patient sample to support physicians in the diagnosis and treatment of a variety of diseases.
Since science and technology are still far away from such a comprehensive and flexible haematology analyzer, this project is a step in the right direction. As described, DHM in combination with a microfluidic channel enables a high throughput of cells and at the same time a relatively detailed view of the composition of the cell and their interior.
Diseases such as leukemia can not only destroy the natural balance of white blood cells, but also cause the release of unfinished precursor cells. Thus, not only the normal subgroups must be distinguished and counted but also the special cases, which cannot be carried out by conventional devices.
This problem also exists in the differentiation and purification of cell cultures, which is hardly possible for the human eye without sample preparation.
In general, image-based detection, classification and analysis of cellular objects offers great potential in the fight against intra- and extracellular parasites. In the case of drug development for schistosomiasis, the viability of worm-like parasites should be monitored. The same applies to the detection and treatment of malaria, in which plasmodia infect red blood cells.
All the above mentioned use cases place the following demands on a predictive artiﬁcial intelligence solution:
- Robust and efficient information retrieval about the digitized cells
- Differentiation and classification of different cell types in suspension on the basis of holographic data
- Analysis of different cell types for different clinical questions: cell cultures, blood cells, intra- and extracellular parasites, ...
The prototypical implementation of the CellPhaser software in this project will take this into account and will be evaluated according to its performance in these areas.
The data basis for blood cell analysis is formed by 3D holograms and the phase images reconstructed from them (cf. Figure 2), which allow insights into the nature of the cell and its interior. The interpretation of these data requires the following steps:
Firstly, it must be determined which image areas contain relevant objects or structures. Then the relevant areas can be prepared for processing by sophisticated machine learning algorithms.
It is generally considered that modern learning algorithms require a large amount of training data. The task of building a suitable ground truth must be supported by the machine.
The main challenges for feature extraction are the different object groups (leukocytes, erythrocytes, tissue cells and parasites), the strongly varying object size and the problem type itself (e.g. malaria or leukemia detection). It is important to have features that are universally and robustly applicable, but do not lose their interpretability.
Depending on the question, e.g. the assignment to a blood cell subgroup, the detection of malaria pathogens or the determination of the viability of parasites, different statements have to be made. The decisions made in this step must be precise and comprehensible in order to use the software effectively.
The acceptance of a machine judgement is based on the transparency of the generating methodology and the linkability with existing forms of presentation.
The implementation of the project is characterized by two aspects. These are the user-centered design to achieve clinical relevance and acceptance as well as the finding of suitable algorithms for robust information retrieval from cell data.
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