Abstract
PathoSpotter is a computational system designed to assist pathologists in teaching about and researching kidney diseases. PathoSpotter-K is the version that was developed to detect nephrological lesions in digital images of kidneys. Here, we present the results obtained using the first version of PathoSpotter-K, which uses classical image processing and pattern recognition methods to detect proliferative glomerular lesions with an accuracy of 88.3 ± 3.6%. Such performance is only achieved by similar systems if they use images of cell in contexts that are much less complex than the glomerular structure. The results indicate that the approach can be applied to the development of systems designed to train pathology students and to assist pathologists in determining large-scale clinicopathological correlations in morphological research.
Highlights
The translation of elementary nephrological lesions into some form of computational language would enable large-scale clinical-pathological associations via a large database and contribute to the training of young pathologists
Compared to similar work in the digital pathology literature, the 88% accuracy achieved by PathoSpotter-K is equivalent to the results obtained by Kothari and colleagues (77%)[26], Sirinukunwattana and colleagues (77%)[23], Schochlin and colleagues (88.9%)[20], and Mathur and colleagues (92%)[18]
We presented PathoSpotter-K, a computational system that assists pathologists in teaching and researching nephrology using digital images of glomeruli
Summary
The translation of elementary nephrological lesions into some form of computational language would enable large-scale clinical-pathological associations via a large database and contribute to the training of young pathologists. We present the early results related to the ability of PathoSpotter-K to identify elementary glomerular lesions. PathoSpotter-K is a branch of the PathoSpotter project, which is an interdisciplinary research project that joins pathologists and computer scientists with the aim of generating computational systems that support research and training in pathology
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
