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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

Examples of brain tumor imaging by endomicroscopy technology. (a)–(c) Glioblastoma (tumor) images. (d)–(f) Meningioma (tumor) images.
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fig3: Examples of brain tumor imaging by endomicroscopy technology. (a)–(c) Glioblastoma (tumor) images. (d)–(f) Meningioma (tumor) images.

Mentions: Specimens were collected from patients undergoing neurosurgical operations at the Department of Neurosurgery of the Hospital Merheim, Cologne Medical Center, in Cologne, Germany. All studies on human subjects were performed according to the requirements of the local ethic committee and in agreement with the Declaration of Helsinki. Tissue samples were excised from the tumor bed after the resection of the tumor. First, 1-2 drops of 0.01 mg/mL acriflavine hydrochloride AF from Sigma Pharmaceuticals, Victoria, Australia, dissolved in saline were administered topically to the excised tissue sample. This is primarily to stain the nuclei and to a minor extent the cell membrane and the extracellular matrix. The excess dye was washed off with saline. In this study, we used probes from Mauna Kea Technologies, Paris, France, to examine tissue samples. The tip of the probe was placed gently on the tissue and a sequence of images was taken. After imaging, human tissue samples were stored in 4% formalin and transferred for histopathology. Preliminary tests showed that neither the fixation process nor the age of the sample or the preoperative administration of 5-ALA has an effect on CLE examination after topical application of AF. Figure 3 shows examples of typical brain tumor imaging by endomicroscopy technology.


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)

Examples of brain tumor imaging by endomicroscopy technology. (a)–(c) Glioblastoma (tumor) images. (d)–(f) Meningioma (tumor) images.
© Copyright Policy
Related In: Results  -  Collection

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

fig3: Examples of brain tumor imaging by endomicroscopy technology. (a)–(c) Glioblastoma (tumor) images. (d)–(f) Meningioma (tumor) images.
Mentions: Specimens were collected from patients undergoing neurosurgical operations at the Department of Neurosurgery of the Hospital Merheim, Cologne Medical Center, in Cologne, Germany. All studies on human subjects were performed according to the requirements of the local ethic committee and in agreement with the Declaration of Helsinki. Tissue samples were excised from the tumor bed after the resection of the tumor. First, 1-2 drops of 0.01 mg/mL acriflavine hydrochloride AF from Sigma Pharmaceuticals, Victoria, Australia, dissolved in saline were administered topically to the excised tissue sample. This is primarily to stain the nuclei and to a minor extent the cell membrane and the extracellular matrix. The excess dye was washed off with saline. In this study, we used probes from Mauna Kea Technologies, Paris, France, to examine tissue samples. The tip of the probe was placed gently on the tissue and a sequence of images was taken. After imaging, human tissue samples were stored in 4% formalin and transferred for histopathology. Preliminary tests showed that neither the fixation process nor the age of the sample or the preoperative administration of 5-ALA has an effect on CLE examination after topical application of AF. Figure 3 shows examples of typical brain tumor imaging by endomicroscopy technology.

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