Toward an automatic tool for oligoclonal band detection in cerebrospinal fluid and tears for multiple sclerosis diagnosis: lane segmentation based on a ribbon univariate open active contour

Autor: Patrick Hautecoeur, Farah Haddad, Nicolas Vandenbroucke, Samuel Boudet, Laurent Peyrodie, G. Forzy
Přispěvatelé: Laboratoire de physique des interfaces et des couches minces [Palaiseau] (LPICM), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS), Unité de traitement des signaux Biomédicaux (UTSB), Université catholique de Lille (UCL), LAGIS-SI, Laboratoire d'Automatique, Génie Informatique et Signal (LAGIS), Université de Lille, Sciences et Technologies-Centrale Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Centrale Lille-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Informatique Signal et Image de la Côte d'Opale (LISIC), Université du Littoral Côte d'Opale (ULCO), Groupe Hospitalier de l'Institut Catholique de Lille (GHICL)
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Electrophoresis
Multiple Sclerosis
Oligoclonal band
Computer science
Computation
0206 medical engineering
Biomedical Engineering
02 engineering and technology
030218 nuclear medicine & medical imaging
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
03 medical and health sciences
0302 clinical medicine
Region of interest
[INFO.INFO-AU]Computer Science [cs]/Automatic Control Engineering
Image Processing
Computer-Assisted
[INFO.INFO-IM]Computer Science [cs]/Medical Imaging
Humans
Segmentation
Computer vision
ComputingMilieux_MISCELLANEOUS
Parametric statistics
Active contour model
Isoelectric focusing
business.industry
Oligoclonal Bands
Scale factor
020601 biomedical engineering
Computer Science Applications
Tears
[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]
Artificial intelligence
business
[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Algorithms
Zdroj: Medical and Biological Engineering and Computing
Medical and Biological Engineering and Computing, Springer Verlag, 2020, 58 (5), pp.967-976. ⟨10.1007/s11517-020-02141-9⟩
ISSN: 0140-0118
1741-0444
DOI: 10.1007/s11517-020-02141-9⟩
Popis: The latest revision of multiple sclerosis diagnosis guidelines emphasizes the role of oligoclonal band detection in isoelectric focusing images of cerebrospinal fluid. Recent studies suggest tears as a promising noninvasive alternative to cerebrospinal fluid. We are developing the first automatic method for isoelectric focusing image analysis and oligoclonal band detection in cerebrospinal fluid and tear samples. The automatic analysis would provide an accurate, fast analysis and would reduce the expert-dependent variability and errors of the current visual analysis. In this paper, we describe a new effective model for the fully automated segmentation of highly distorted lanes in isoelectric focusing images. This approach is a new formulation of the classic parametric active contour problem, in which an open active contour is constrained to move from the top to the bottom of the image, and the x-axis coordinate is expressed as a function of the y-axis coordinate. The left and right edges of the lane evolved together in a ribbon-like shape so that the full width of the lane was captured reliably. The segmentation algorithm was implemented using a multiresolution approach in which the scale factor and the active contour control points were progressively increased. The lane segmentation algorithm was tested on a database of 51 isoelectric focusing images containing 419 analyzable lanes. The new model gave robust results for highly curved lanes, weak edges, and low-contrast lanes. A total of 98.8% of the lanes were perfectly segmented, and the remaining 1.2% had only minor errors. The computation time (1 s per membrane) is negligible. This method precisely defines the region of interest in each lane and thus is a major step toward the first fully automatic tool for oligoclonal band detection in isoelectric focusing images. Graphical abstract.
Databáze: OpenAIRE
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