Cell proposal network for microscopy image analysis

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussavertaisarvioitu



  • University of Oulu


Robust cell detection plays a key role in the development of reliable methods for automated analysis of microscopy images. It is a challenging problem due to low contrast, variable fluorescence, weak boundaries, conjoined and overlapping cells, causing most cell detection methods to fail in difficult situations. One approach for overcoming these challenges is to use cell proposals, which enable the use of more advanced features from ambiguous regions and/or information from adjacent frames to make better decisions. However, most current methods rely on simple proposal generation and scoring methods, which limits the performance they can reach. In this paper, we propose a convolutional neural network based method which generates cell proposals to facilitate cell detection, segmentation and tracking. We compare our method against commonly used proposal generation and scoring methods and show that our method generates significantly better proposals, and achieves higher final recall and average precision.


Otsikko2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings
TilaJulkaistu - 3 elokuuta 2016
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaIEEE International Conference on Image Processing - Phoenix, Yhdysvallat
Kesto: 25 syyskuuta 201628 syyskuuta 2016
Konferenssinumero: 23


ConferenceIEEE International Conference on Image Processing

ID: 10319716