title: Towards semantic-driven high-content image analysis: an operational instantiation for mitosis detection in digital histopathology.
abstract: This study concerns a novel symbolic cognitive vision framework emerged from the Cognitive Microscopy (MICO(1)) initiative. MICO aims at supporting the evolution towards digital pathology, by studying cognitive clinical-compliant protocols involving routine virtual microscopy. We instantiate this paradigm in the case of mitotic count as a component of breast cancer grading in histopathology. The key concept of our approach is the role of the semantics as driver of the whole slide image analysis protocol. All the decisions being taken into a semantic and formal world, MICO represents a knowledge-driven platform for digital histopathology. Therefore, the core of this initiative is the knowledge representation and the reasoning. Pathologists' knowledge and strategies are used to efficiently guide image analysis algorithms. In this sense, hard-coded knowledge, semantic and usability gaps are to be reduced by a leading, active role of reasoning and of semantic approaches. Integrating ontologies and reasoning in confluence with modular imaging algorithms, allows the emergence of new clinical-compliant protocols for digital pathology. This represents a promising way to solve decision reproducibility and traceability issues in digital histopathology, while increasing the flexibility of the platform and pathologists' acceptance, the one always having the legal responsibility in the diagnosis process. The proposed protocols open the way to increasingly reliable cancer assessment (i.e. multiple slides per sample analysis), quantifiable and traceable second opinion for cancer grading, and modern capabilities for cancer research support in histopathology (i.e. content and context-based indexing and retrieval). Last, but not least, the generic approach introduced here is applicable for number of additional challenges, related to molecular imaging and, in general, to high-content image exploration.
authors.affiliation: Sorbonne Universités, UPMC Univ Paris 06, LIB - UMR 7371 - UMR_S 1146, F-75013 Paris, France; CNRS, UMR 7371, Laboratoire d'Imagerie Biomédicale, F-75013 Paris, France; INSERM, UMR_S 1146, Laboratoire d'Imagerie Biomédicale, F-75013 Paris, France. Electronic address: daniel.racoceanu@upmc.fr.
authors.forename: D
authors.initials: D
authors.lastname: Racoceanu
authors.affiliation: Sorbonne Universités, UPMC Pars 06, UH Pitié-Salpêtrière-CFx, Department of Pathology APHP, UIMAP, F-75013 Paris, France.
authors.forename: F
authors.initials: F
authors.lastname: Capron
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000473:pathology*; D002467:Cell Nucleus / Q000473:pathology*; D005260:Female; D006652:Histological Techniques / Q000379:methods; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D000069550:Machine Learning; D008853:Microscopy / Q000379:methods*; D008938:Mitosis; D060787:Neoplasm Grading; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012660:Semantics; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D012984:Software; D014584:User-Computer Interface
keywords: Breast cancer; Cancer grading; Cognitive microscopy; High-content image exploration; Medical ontologies; Semantic reasoning; Virtual microscopy