title: Automatic Tissue Differentiation Based on Confocal Endomicroscopic Images for Intraoperative Guidance in Neurosurgery.
abstract: Diagnosis of tumor and definition of tumor borders intraoperatively using fast histopathology is often not sufficiently informative primarily due to tissue architecture alteration during sample preparation step. Confocal laser microscopy (CLE) provides microscopic information of tissue in real-time on cellular and subcellular levels, where tissue characterization is possible. One major challenge is to categorize these images reliably during the surgery as quickly as possible. To address this, we propose an automated tissue differentiation algorithm based on the machine learning concept. During a training phase, a large number of image frames with known tissue types are analyzed and the most discriminant image-based signatures for various tissue types are identified. During the procedure, the algorithm uses the learnt image features to assign a proper tissue type to the acquired image frame. We have verified this method on the example of two types of brain tumors: glioblastoma and meningioma. The algorithm was trained using 117 image sequences containing over 27 thousand images captured from more than 20 patients. We achieved an average cross validation accuracy of better than 83%. We believe this algorithm could be a useful component to an intraoperative pathology system for guiding the resection procedure based on cellular level information.
authors.affiliation: Siemens Healthcare, Technology Center, Princeton, NJ 08540, USA.
authors.forename: Ali
authors.initials: A
authors.lastname: Kamen
authors.affiliation: Siemens Healthcare, Technology Center, Princeton, NJ 08540, USA.
authors.forename: Shanhui
authors.initials: S
authors.lastname: Sun
authors.affiliation: Siemens Healthcare, Technology Center, Princeton, NJ 08540, USA.
authors.forename: Shaohua
authors.initials: S
authors.lastname: Wan
authors.affiliation: Siemens Healthcare, Technology Center, Princeton, NJ 08540, USA.
authors.forename: Stefan
authors.initials: S
authors.lastname: Kluckner
authors.affiliation: Siemens Healthcare, Technology Center, Princeton, NJ 08540, USA.
authors.forename: Terrence
authors.initials: T
authors.lastname: Chen
authors.affiliation: Siemens Corporate Technology, 81739 Munich, Germany.
authors.forename: Alexander M
authors.initials: AM
authors.lastname: Gigler
authors.affiliation: Siemens Corporate Technology, 81739 Munich, Germany.
authors.forename: Elfriede
authors.initials: E
authors.lastname: Simon
authors.affiliation: Siemens Corporate Technology, 81739 Munich, Germany.
authors.forename: Maximilian
authors.initials: M
authors.lastname: Fleischer
authors.affiliation: Department of Neurosurgery, Hospital Merheim, Cologne Medical Center, 51109 Cologne, Germany; Department of Neurosurgery, Heinrich Heine University Düsseldorf, 40255 Düsseldorf, Germany.
authors.forename: Mehreen
authors.initials: M
authors.lastname: Javed
authors.affiliation: Department of Neurosurgery, Hospital Merheim, Cologne Medical Center, 51109 Cologne, Germany; Department of Neurosurgery, Heinrich Heine University Düsseldorf, 40255 Düsseldorf, Germany.
authors.forename: Samira
authors.initials: S
authors.lastname: Daali
authors.affiliation: Department of Neurosurgery, Hospital Merheim, Cologne Medical Center, 51109 Cologne, Germany.
authors.forename: Alhadi
authors.initials: A
authors.lastname: Igressa
authors.affiliation: Department of Neurosurgery, Hospital Merheim, Cologne Medical Center, 51109 Cologne, Germany; Department of Neurosurgery, Heinrich Heine University Düsseldorf, 40255 Düsseldorf, Germany.
authors.forename: Patra
authors.initials: P
authors.lastname: Charalampaki
mesh_terms: D001932:Brain Neoplasms / Q000000981:diagnostic imaging / Q000473:pathology* / Q000601:surgery; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D000069416:Intravital Microscopy / Q000379:methods; D000069550:Machine Learning; D018613:Microscopy, Confocal / Q000379:methods*; D008866:Microsurgery / Q000379:methods*; D044583:Neuroendoscopy / Q000379:methods*; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D025321:Surgery, Computer-Assisted / Q000379:methods*