Tesseract-medical imaging: open-source browser-based platform for artificial intelligence deployment in medical imaging

Artificial Intelligence (AI) is increasingly becoming a tool to enhance various medical image analysis tasks with accuracies comparable to expert clinicians. Computer assisted detection and diagnosis, and image segmentation and registration have significantly benefited from AI. However, integration of AI into the clinical workflow has been slow due to requirements for libraries that are specific to each model, and also environments that are specific to clinical centers. These challenges demonstrate the need for an AI-based solution that can be integrated into any environment with minimum hardware and software overhead. Tesseract-Medical Imaging (Tesseract-MI) is an open-source, web-based platform which enables deployment of AI models while simultaneously providing standard image viewing and reporting schemes. The goal of Tesseract-MI is to augment 3D medical imaging and provide a 4th dimension (AI) when requested by a user. As a case study, we demonstrate the utility of our platform and present ProstateCancer.ai, a web application for identification of clinically significant prostate cancer in MRI.

[1]  Mert R. Sabuncu,et al.  Unsupervised Learning for Fast Probabilistic Diffeomorphic Registration , 2018, MICCAI.

[2]  William M. Wells,et al.  Semi-Supervised Deep Metrics for Image Registration , 2018, ArXiv.

[3]  Dmitrii Bychkov,et al.  Deep learning based tissue analysis predicts outcome in colorectal cancer , 2018, Scientific Reports.

[4]  Stephen M. Moore,et al.  The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository , 2013, Journal of Digital Imaging.

[5]  Kemal Tuncali,et al.  Automatic Needle Segmentation and Localization in MRI With 3-D Convolutional Neural Networks: Application to MRI-Targeted Prostate Biopsy , 2019, IEEE Transactions on Medical Imaging.

[6]  Gabor Fichtinger,et al.  dcmqi: An Open Source Library for Standardized Communication of Quantitative Image Analysis Results Using DICOM. , 2017, Cancer research.

[7]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Shekoofeh Azizi,et al.  Exploring microRNA Regulation of Cancer with Context-Aware Deep Cancer Classifier , 2018, PSB.

[9]  Purang Abolmaesumi,et al.  DeepInfer: open-source deep learning deployment toolkit for image-guided therapy , 2017, Medical Imaging.

[10]  Fausto Milletari TOMAAT: volumetric medical image analysis as a cloud service , 2018, ArXiv.

[11]  M. Parmar,et al.  Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating confi rmatory study , 2018 .

[12]  Purang Abolmaesumi,et al.  Classification of clinical significance of MRI prostate findings using 3D convolutional neural networks , 2017, Medical Imaging.