Alexander Michael Gigler

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

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.

Towards an Efficient Computational Framework for Guiding Surgical Resection through Intra-operative Endo-microscopic Pathology

Precise detection and surgical resection of the tumors during an operation greatly increases the chance of the overall procedure efficacy. Emerging experimental in-vivo imaging technologies such as Confocal Laser Endomicroscopy CLE, could potentially assist surgeons to examine brain tissues on histological scale in real-time during the operation. However, it is a challenging task for neurosurgeons to interpret these images in real-time, primarily due to the low signal to noise ratio and variability in the patterns expressed within these images by various examined tissue types. In this paper, we present a comprehensive computational framework capable of automatic brain tumor classification in real-time. Specifically, our contributions include: a an end-to-end computational pipeline where a variety of the feature extraction methods, encoding schemes, and classification algorithms can be readily deployed, b thorough evaluation of state-of-the-art low-level image features and popular encoding techniques in context of CLE imagery, and finally, c A highly optimized feature pooling method based on codeword proximity. The proposed system can effectively classify two types of commonly diagnosed brain tumors in CLE sequences captured in real-time with close to 90% accuracy. Extensive experiments on a dataset of 117 videos demonstrate the efficacy of our system.