Diagnosis of more than 80% of the haematological diseases is based on morphological studies in blood. Objectivity and skilled expertise are needed. Digital Imaging has developed and expanded tremendously in laboratory haematology. Nevertheless, automatic analysers do not recognize abnormal blood cells.
We offer solutions for the automatic recognition of blood cell digital images being able to distinguish reactive lymphocytes in infections versus blast cells in acute leukaemia and abnormal lymphoid neoplastic cells in lymphoma patients: CellsiMatic software.
Other solutions are under development, such as automatic classification of different types of blast cells, dysplastic cell recognition and malaria or other inclusion detection in red cells.
Our personnel have backgrounds in clinical pathology, laboratory of haematology, cytology, applied mathematics, statistics, biomedical engineering, image analysis, machine learning and deep learning. Our goal is to contribute within this scenario in the following directions: research, technological tools, courses, teaching activities and consulting services.
Dra Anna Merino presented an Elevator Pitch at the 2ª Clinic Barcelona Summer School. She explained about the software developed by CellsiLab, based on deep learning, which can be a new tool assisting to the clinical pathologist to the detection of leukaemic cells in blood....
In October 2019 the second edition of the book: “Citología de sangre Periférica y Líquidos Biológicos” has been published by the Editorial Panamericana. The new edition contains 10 chapters, which explain the most relevant morphological alterations concerning red blood cells, leukocytes and platelets; microorganisms and......