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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

Example of excluded images due to low entropy.
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fig5: Example of excluded images due to low entropy.

Mentions: Image frames with low image texture information are not clinically interesting or not discriminative for image classification task. Image entropy [11] is a quantity which is used to describe the “informativeness” of an image region, that is, the amount of information contained in a region. On the one hand, low-entropy images have very little contrast where large numbers of pixels have the same or similar intensity values. On the other hand high entropy images have a great deal of contrast from one pixel to the next. See examples of images with low information content in Figure 5.


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)

Example of excluded images due to low entropy.
© Copyright Policy
Related In: Results  -  Collection

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

fig5: Example of excluded images due to low entropy.
Mentions: Image frames with low image texture information are not clinically interesting or not discriminative for image classification task. Image entropy [11] is a quantity which is used to describe the “informativeness” of an image region, that is, the amount of information contained in a region. On the one hand, low-entropy images have very little contrast where large numbers of pixels have the same or similar intensity values. On the other hand high entropy images have a great deal of contrast from one pixel to the next. See examples of images with low information content in Figure 5.

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