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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

The performance of the majority voting based classification with respect to time window size.
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fig7: The performance of the majority voting based classification with respect to time window size.

Mentions: We verify majority voting idea using the brain tumor dataset, with the same experimental setup as described in the previous sections. We set the sliding time window to be T in length and derive the class label for the current frame using the majority voting result of the frames within the sliding time window. The recognition performance with respect to the time window length T is given in Figure 7. As it can be seen the optimal performance is achieved at T = 5. It is quite likely that higher recognition accuracy can be achieved using much longer time window. In practice, however, one has to balance the relative importance between recognition speed and accuracy. Table 1 depicts the results from three coding approaches including BoW, LLC, and LSC. The results include the classification metrics as well as the processing time per frame. As it can be seen form the table, the LSC method is the most accurate. However, it does come with a heavy computational cost. The future work includes improving the quality of the classification and also improving the performance of the algorithm to achieve real-time response.


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)

The performance of the majority voting based classification with respect to time window size.
© Copyright Policy
Related In: Results  -  Collection

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

fig7: The performance of the majority voting based classification with respect to time window size.
Mentions: We verify majority voting idea using the brain tumor dataset, with the same experimental setup as described in the previous sections. We set the sliding time window to be T in length and derive the class label for the current frame using the majority voting result of the frames within the sliding time window. The recognition performance with respect to the time window length T is given in Figure 7. As it can be seen the optimal performance is achieved at T = 5. It is quite likely that higher recognition accuracy can be achieved using much longer time window. In practice, however, one has to balance the relative importance between recognition speed and accuracy. Table 1 depicts the results from three coding approaches including BoW, LLC, and LSC. The results include the classification metrics as well as the processing time per frame. As it can be seen form the table, the LSC method is the most accurate. However, it does come with a heavy computational cost. The future work includes improving the quality of the classification and also improving the performance of the algorithm to achieve real-time response.

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