Development of a real-time tactile sensing system for brain tumor diagnosis

2010 ◽  
Vol 5 (4) ◽  
pp. 359-367 ◽  
Author(s):  
Yoshihiro Tanaka ◽  
Qingyun Yu ◽  
Kazuki Doumoto ◽  
Akihito Sano ◽  
Yuichiro Hayashi ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Real Time ◽  
Tumor Diagnosis ◽  
Tactile Sensing ◽  
Sensing System ◽  
Brain Tumor Diagnosis

scholarly journals Near Real-Time Intraoperative Brain Tumor Diagnosis Using Stimulated Raman Histology and Deep Neural Networks

Neurosurgery ◽  
2019 ◽  
Vol 66 (Supplement_1) ◽  
Author(s):  
Todd C Hollon ◽  
Balaji Pandian ◽  
Siri Sahib Singh Khalsa ◽  
Randy D’Amico ◽  
Michael B Sisti ◽  
...  
Keyword(s):  
Neural Networks ◽  
Brain Tumor ◽  
Diagnostic Accuracy ◽  
Real Time ◽  
Cancer Surgery ◽  
Tumor Diagnosis ◽  
Imaging Method ◽  
Intraoperative Diagnosis ◽  
Brain Tumor Diagnosis ◽  
Stimulated Raman

Abstract INTRODUCTION Intraoperative diagnosis is essential for providing safe and effective care during cancer surgery. The existing workflow for intraoperative diagnosis based on H&E staining of processed tissue is time-, resource-, and labor-intensive. Moreover, interpretation of intraoperative histologic images is dependent on a pathology workforce that is contracting and unevenly distributed across the centers where cancer surgery is performed worldwide. METHODS We developed an automated workflow, independent of traditional H&E histology, that combines stimulated Raman histology (SRH), a rapid label-free optical imaging method, and deep convolutional neural networks (CNN) to predict diagnosis at the bedside in near real time. Specifically, our CNN, trained on over 2.5 million SRH images, predicts brain tumor diagnosis in the operating room in under 150 s, which is an order of magnitude faster than conventional techniques (eg, 20-30 min). RESULTS To validate our workflow in the clinical setting, we designed a multicenter, prospective, noninferiority clinical trial (N = 204) that compares SRH plus CNN vs traditional H&E histology. Primary endpoint was overall diagnostic accuracy. We show that CNN-based diagnosis of SRH images was noninferior to pathologist-based interpretation of conventional histologic images (overall accuracy, 94.6% vs 95.5%). Additionally, our CNN learned a hierarchy of interpretable histologic feature representations to classify the major histopathologic classes of brain tumors. We then developed and implemented a semantic segmentation method that can identify tumor infiltrated and diagnostic regions within SRH images. Mean intersection over union values was 61 ± 28.6 for ground truth diagnostic class and 86.0 ± 28.6 for tumor-infiltrated regions. CONCLUSION We have demonstrated how combining bedside optical histology with deep learning can result in near real-time intraoperative brain tumor diagnosis. Our workflow provides a means of delivering expert-level intraoperative diagnosis where neuropathology resources are scarce and improve diagnostic accuracy in resource-rich centers.

scholarly journals Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks

2020 ◽  
Vol 26 (1) ◽  
pp. 52-58 ◽  
Author(s):  
Todd C. Hollon ◽  
Balaji Pandian ◽  
Arjun R. Adapa ◽  
Esteban Urias ◽  
Akshay V. Save ◽  
...  
Keyword(s):  
Neural Networks ◽  
Brain Tumor ◽  
Real Time ◽  
Deep Neural Networks ◽  
Tumor Diagnosis ◽  
Brain Tumor Diagnosis ◽  
Stimulated Raman

Smear technique in the intra-operative brain tumor diagnosis: Its advantages and limitations

1999 ◽  
Vol 21 (1) ◽  
pp. 121-124 ◽  
Author(s):  
J. Slowiński ◽  
M. Harabin-Slowińska ◽  
R. Mrówka
Keyword(s):  
Brain Tumor ◽  
Tumor Diagnosis ◽  
Brain Tumor Diagnosis

scholarly journals Cancer Imaging and Brain Tumor Diagnosis

2017 ◽  
Vol 09 (01) ◽  
Author(s):  
Hamidreza Shirzadfar ◽  
Samin Riahi ◽  
Mahsa Sadat Ghaziasgar
Keyword(s):  
Brain Tumor ◽  
Cancer Imaging ◽  
Tumor Diagnosis ◽  
Brain Tumor Diagnosis
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