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SUMMARY:Image analysis tools for cancer biology: how could we make it bett
 er? - Patrice Mascalchi. Cancer Research UK Cambridge Institute.
DTSTART:20150211T140000Z
DTEND:20150211T150000Z
UID:TALK57752@talks.cam.ac.uk
CONTACT:38916
DESCRIPTION:The past decades have seen a large democratisation of image an
 alysis software either commercial or freely distributed\, allowing segment
 ation of objects of interest. However\, as the dimensions and the resoluti
 on of images increase with the new microscopy techniques\, scientists’ d
 emands would require more powerful and flexible tools to extract data from
  features of interest.\n\nIn the case of biological studies\, it is necess
 ary to compare the assessed specimen to a negative control and then show s
 tatistical relevance of the difference. So far\, image analysis tools are 
 successful in segmenting simple objects\, i.e. oval shapes\, filaments\, s
 mall dots\, etc.\, in a neutral surrounding. Still\, the main issues relat
 ed to the imaging of biological samples\, i.e. noise\, non-specific backgr
 ound or heterogeneous signal\, often hinder the detection of the features 
 of interest. Improving strategies in de-noising\, shape modelling or machi
 ne-learning could for instance help achieving more precise measurements\, 
 giving a better answer to the biological question.\n\nThe aim of this pres
 entation is to briefly introduce some examples of image analysis studies c
 onducted at the CRUK Cambridge Institute\, stressing the respective succes
 ses and difficulties. To a broader extent\, we will discuss about the way 
 biologists\, mathematicians and computer scientists could better link thei
 r respective skills and knowledge. 
LOCATION:MR4\, Centre for Mathematical Sciences\, Wilberforce Road\, Cambr
 idge
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