title: ANAlyte: A modular image analysis tool for ANA testing with indirect immunofluorescence.
abstract: OBJECTIVE
  The automated analysis of indirect immunofluorescence images for Anti-Nuclear Autoantibody (ANA) testing is a fairly recent field that is receiving ever-growing interest from the research community. ANA testing leverages on the categorization of intensity level and fluorescent pattern of IIF images of HEp-2 cells to perform a differential diagnosis of important autoimmune diseases. Nevertheless, it suffers from tremendous lack of repeatability due to subjectivity in the visual interpretation of the images. The automatization of the analysis is seen as the only valid solution to this problem. Several works in literature address individual steps of the work-flow, nonetheless integrating such steps and assessing their effectiveness as a whole is still an open challenge.
  
  
  METHODS
  We present a modular tool, ANAlyte, able to characterize a IIF image in terms of fluorescent intensity level and fluorescent pattern without any user-interactions. For this purpose, ANAlyte integrates the following: (i) Intensity Classifier module, that categorizes the intensity level of the input slide based on multi-scale contrast assessment; (ii) Cell Segmenter module, that splits the input slide into individual HEp-2 cells; (iii) Pattern Classifier module, that determines the fluorescent pattern of the slide based on the pattern of the individual cells.
  
  
  RESULTS
  To demonstrate the accuracy and robustness of our tool, we experimentally validated ANAlyte on two different public benchmarks of IIF HEp-2 images with rigorous leave-one-out cross-validation strategy. We obtained overall accuracy of fluorescent intensity and pattern classification respectively around 85% and above 90%. We assessed all results by comparisons with some of the most representative state of the art works.
  
  
  CONCLUSIONS
  Unlike most of the other works in the recent literature, ANAlyte aims at the automatization of all the major steps of ANA image analysis. Results on public benchmarks demonstrate that the tool can characterize HEp-2 slides in terms of intensity and fluorescent pattern with accuracy better or comparable with the state of the art techniques, even when such techniques are run on manually segmented cells. Hence, ANAlyte can be proposed as a valid solution to the problem of ANA testing automatization.
authors.affiliation: Dept. of Computer and Control Engineering, Politecnico di Torino, Cso Duca degli Abruzzi 24, 10129 Torino, Italy. Electronic address: santa.dicataldo@polito.it.
authors.forename: Santa
authors.initials: S
authors.lastname: Di Cataldo
authors.affiliation: Dept. of Computer and Control Engineering, Politecnico di Torino, Cso Duca degli Abruzzi 24, 10129 Torino, Italy.
authors.forename: Simone
authors.initials: S
authors.lastname: Tonti
authors.affiliation: Dept. of Computer and Control Engineering, Politecnico di Torino, Cso Duca degli Abruzzi 24, 10129 Torino, Italy.
authors.forename: Andrea
authors.initials: A
authors.lastname: Bottino
authors.affiliation: Dept. of Computer and Control Engineering, Politecnico di Torino, Cso Duca degli Abruzzi 24, 10129 Torino, Italy.
authors.forename: Elisa
authors.initials: E
authors.lastname: Ficarra
mesh_terms: D000465:Algorithms; D001323:Autoantibodies / Q000737:chemistry*; D001327:Autoimmune Diseases / Q000000981:diagnostic imaging*; D016208:Databases, Factual; D016002:Discriminant Analysis; D004847:Epithelial Cells / Q000378:metabolism; D019084:Fluorescent Antibody Technique, Indirect; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008856:Microscopy, Fluorescence; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D057188:Workflow
chemical_list: D001323:Autoantibodies
keywords: ANA testing; Automated characterization of HEp-2 slides; Indirect immunofluorescence; Microscopy image processing