Treatment decisions for patients with presumed glioblastoma are based on tumor characteristics available from a preoperative MR scan. Tumor characteristics, including volume, location, and resectability, are often estimated or manually delineated. This process is time consuming and subjective. Hence, comparison across cohorts, trials, or registries are subject to assessment bias. In this study, we propose a standardized Glioblastoma Surgery Imaging Reporting and Data System (GSI-RADS) based on an automated method of tumor segmentation that provides standard reports on tumor features that are potentially relevant for glioblastoma surgery. As clinical validation, we determine the agreement in extracted tumor features between the automated method and the current standard of manual segmentations from routine clinical MR scans before treatment. In an observational consecutive cohort of 1596 adult patients with a first time surgery of a glioblastoma from 13 institutions, we segmented gadolinium-enhanced tumor parts both by a human rater and by an automated algorithm. Tumor features were extracted from segmentations of both methods and compared to assess differences, concordance, and equivalence. The laterality, contralateral infiltration, and the laterality indices were in excellent agreement. The native and normalized tumor volumes had excellent agreement, consistency, and equivalence. Multifocality, but not the number of foci, had good agreement and equivalence. The location profiles of cortical and subcortical structures were in excellent agreement. The expected residual tumor volumes and resectability indices had excellent agreement, consistency, and equivalence. Tumor probability maps were in good agreement. In conclusion, automated segmentations are in excellent agreement with manual segmentations and practically equivalent regarding tumor features that are potentially relevant for neurosurgical purposes. Standard GSI-RADS reports can be generated by open access software.

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Deep convolutional neural networks have quickly become the standard for medical image analysis. Although there are many frameworks focusing on training neural networks, there are few that focus on high performance inference and visualization of medical images. Neural network inference requires an inference engine (IE), and there are currently several IEs available including Intel's OpenVINO, NVIDIA's TensorRT, and Google's TensorFlow which supports multiple backends, including NVIDIA's cuDNN, AMD's ROCm and Intel's MKL-DNN. These IEs only work on specific processors and have completely different application programming interfaces (APIs). In this paper, we presents methods for extending FAST, an open-source high performance framework for medical imaging, to use any IE with a common programming interface. Thereby making it easier for users to deploy and test their neural networks on different processors. This article provides an overview of current IEs and how they can be combined with existing software such as FAST. The methods are demonstrated and evaluated on three performance demanding medical use cases: real-time ultrasound image segmentation, computed tomography (CT) volume segmentation, and patch-wise classification of whole slide microscopy images. Runtime performance was measured on the three use cases with several different IEs and processors. This revealed that the choice of IE and processor can affect performance of medical neural network image analysis considerably. In the most extreme case of processing 171 ultrasound frames, the difference between the fastest and slowest configuration were half a second vs. 24 seconds. For volume processing, using the CPU or the GPU, showed a difference of 2 vs. 53 seconds, and for processing an whole slide microscopy image, the difference was 81 seconds vs. almost 16 minutes. Source code, binary releases and test data can be found online on GitHub at https://github.com/smistad/FAST/.

Deep convolutional neural networks (CNNs) are the current state-of-the-art for digital analysis of histopathological images. The large size of whole-slide microscopy images (WSIs) requires advanced memory handling to read, display and process these images. There are several open-source platforms for working with WSIs, but few support deployment of CNN models. These applications use thirdparty solutions for inference, making them less user-friendly and unsuitable for high-performance image analysis. To make deployment of CNNs user-friendly and feasible on low-end machines, we have developed a new platform, FastPathology, using the FAST framework and C++. It minimizes memory usage for reading and processing WSIs, deployment of CNN models, and real-time interactive visualization of results. Runtime experiments were conducted on four different use cases, using different architectures, inference engines, hardware configurations and operating systems. Memory usage for reading, visualizing, zooming and panning a WSI were measured, using FastPathology and three existing platforms. FastPathology performed similarly in terms of memory to the other C++-based application, while using considerably less than the two Java-based platforms. The choice of neural network model, inference engine, hardware and processors influenced runtime considerably. Thus, FastPathology includes all steps needed for efficient visualization and processing of WSIs in a single application, including inference of CNNs with real-time display of the results. Source code, binary releases, video demonstrations and test data can be found online