Affiliations: Laboratoire d’Etudes et de Recherche sur les Matériaux, UPRESA CNRS 6004, Institut des Sciences de la Matière et du Rayonnement, Bld du Mal Juin, 14050 Caen Cedex, France | Service d’Anatomie Pathologique, Centre Régional François Baclesse, Caen, France | Laboratoire de Neurosciences, UMR CNRS 6551, Caen, France | Groupement de Recherches et d’Etudes en Instrumentation, Informatique et Images, UPRESA CNRS 6072, Caen, France | Institut Universitaire de Technologie, Département de Mesures Physiques, Caen, France | Laboratoire Universitaire des Sciences Appliquées de Cherbourg, France
Note: [] Corresponding author.
Abstract: The aim of the present study is to propose alternative automatic methods to time consuming interactive sorting of elements for DNA ploidy measurements. One archival brain tumour and two archival breast carcinoma were studied, corresponding to 7120 elements (3764 nuclei, 3356 debris and aggregates). Three automatic classification methods were tested to eliminate debris and aggregates from DNA ploidy measurements (mathematical morphology (MM), multiparametric analysis (MA) and neural network (NN)). Performances were evaluated by reference to interactive sorting. The results obtained for the three methods concerning the percentage of debris and aggregates automatically removed reach 63, 75 and 85% for MM, MA and NN methods, respectively, with false positive rates of 6, 21 and 25%. Information about DNA ploidy abnormalities were globally preserved after automatic elimination of debris and aggregates by MM and MA methods as opposed to NN method, showing that automatic classification methods can offer alternatives to tedious interactive elimination of debris and aggregates, for DNA ploidy measurements of archival tumours.
