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Automatic Tissue Differentiation Based on Confocal Endomicroscopic Images for Intraoperative Guidance in Neurosurgery.

Kamen A, Sun S, Wan S, Kluckner S, Chen T, Gigler AM, Simon E, Fleischer M, Javed M, Daali S, Igressa A, Charalampaki P - Biomed Res Int (2016)

Bottom Line: One major challenge is to categorize these images reliably during the surgery as quickly as possible.We have verified this method on the example of two types of brain tumors: glioblastoma and meningioma.We achieved an average cross validation accuracy of better than 83%.

View Article: PubMed Central - PubMed

Affiliation: Siemens Healthcare, Technology Center, Princeton, NJ 08540, USA.

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.

No MeSH data available.


Related in: MedlinePlus

Potential application of intraoperative pathology and surgical guidance within a hybrid OR (this concept is an investigational tool and not approved for clinical use).
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fig2: Potential application of intraoperative pathology and surgical guidance within a hybrid OR (this concept is an investigational tool and not approved for clinical use).

Mentions: A number of recently introduced optical imaging technologies have started to be utilized in the clinical setting both macroscopically and microscopically during surgeries. Endomicroscopy is a technique for obtaining histology-like images from inside the human body in real-time [2], process known as “optical biopsy” [3]. It generally refers to fluorescence confocal microscopy, although multiphoton microscopy and optical coherence tomography have also been adapted for endoscopic use [4]. These images provide abundant information regarding cellular, vascular, and connective tissue structures and specific descriptors which could be used to differentiate various tissue types [5]. Our aim is to be able to computerize these analyses and assist surgeons in delineating tissue boundaries by analyzing real-time streams of images as quickly as possible (see Figures 1 and 2).


Automatic Tissue Differentiation Based on Confocal Endomicroscopic Images for Intraoperative Guidance in Neurosurgery.

Kamen A, Sun S, Wan S, Kluckner S, Chen T, Gigler AM, Simon E, Fleischer M, Javed M, Daali S, Igressa A, Charalampaki P - Biomed Res Int (2016)

Potential application of intraoperative pathology and surgical guidance within a hybrid OR (this concept is an investigational tool and not approved for clinical use).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4835625&req=5

fig2: Potential application of intraoperative pathology and surgical guidance within a hybrid OR (this concept is an investigational tool and not approved for clinical use).
Mentions: A number of recently introduced optical imaging technologies have started to be utilized in the clinical setting both macroscopically and microscopically during surgeries. Endomicroscopy is a technique for obtaining histology-like images from inside the human body in real-time [2], process known as “optical biopsy” [3]. It generally refers to fluorescence confocal microscopy, although multiphoton microscopy and optical coherence tomography have also been adapted for endoscopic use [4]. These images provide abundant information regarding cellular, vascular, and connective tissue structures and specific descriptors which could be used to differentiate various tissue types [5]. Our aim is to be able to computerize these analyses and assist surgeons in delineating tissue boundaries by analyzing real-time streams of images as quickly as possible (see Figures 1 and 2).

Bottom Line: One major challenge is to categorize these images reliably during the surgery as quickly as possible.We have verified this method on the example of two types of brain tumors: glioblastoma and meningioma.We achieved an average cross validation accuracy of better than 83%.

View Article: PubMed Central - PubMed

Affiliation: Siemens Healthcare, Technology Center, Princeton, NJ 08540, USA.

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.

No MeSH data available.


Related in: MedlinePlus