DeepInfer: open-source deep learning deployment toolkit for image-guided therapy

Abstract

Deep learning models have outperformed some of the previous state-of-the-art approaches in medical image analysis. Instead of using hand-engineered features, deep models attempt to automatically extract hierarchical representations at multiple levels of abstraction from the data. Therefore, deep models are usually considered to be more flexible and robust solutions for image analysis problems compared to conventional computer vision models. They have demonstrated significant improvements in computer-aided diagnosis and automatic medical image analysis applied to such tasks as image segmentation, classification and registration. However, deploying deep learning models often has a steep learning curve and requires detailed knowledge of various software packages. Thus, many deep models have not been integrated into the clinical research workflows causing a gap between the state-of-the-art machine learning in medical applications and evaluation in clinical research procedures. In this paper, we propose “DeepInfer” – an open-source toolkit for developing and deploying deep learning models within the 3D Slicer medical image analysis platform. Utilizing a repository of task-specific models, DeepInfer allows clinical researchers and biomedical engineers to deploy a trained model selected from the public registry, and apply it to new data without the need for software development or configuration. As two practical use cases, we demonstrate the application of DeepInfer in prostate segmentation for targeted MRI-guided biopsy and identification of the target plane in 3D ultrasound for spinal injections.

Toward a real-time system for temporal enhanced ultrasound-guided prostate biopsy

“…Prior work reported on prostate segmentation using deep learning within 3D Slicer [22] is only suitable for static MRI data, not dynamic ultrasound images which are streamed in real time from an ultrasound system.…”

“…To the best of our knowledge, this paper is also the first report on real-time automatic segmentation of prostate ultrasound images using deep learning. Prior work reported on prostate segmentation using deep learning within 3D Slicer [22] is only suitable for static MRI data, not dynamic ultrasound images which are streamed in real time from an ultrasound system.…”

Accurate model-based segmentation of gynecologic brachytherapy catheter collections in MRI-images

“…Since completing the work reported in this paper where features to encode catheter artifacts were ”hand crafted” based on the authors’ observations of several examples, we have begun investigating the training of deep convolutional neural networks (CNN) to the task by leveraging insights from (Ghafoorian et al, 2017; Mehrtash et al, 2017). This initiative covers tip detection which could be used to seed the presented method, as well as an alternative catheter detection method.…”

R2D2: A scalable deep learning toolkit for medical imaging segmentation

“…The DLTK 53 is another open source TensorFlow-based medical imaging toolkit implementing baseline versions classic network architectures. DeepInfer 54 is an additional DLTK for image-guided therapy with a focus on the deployment and the reuse of pretrained models.…”