Slicer-DeepSeg: Open-Source Deep Learning Toolkit for Brain Tumour Segmentation

Purpose Computerized medical imaging processing assists neurosurgeons to localize tumours precisely. It plays a key role in recent image-guided neurosurgery. Hence, we developed a new open-source toolkit, namely Slicer-DeepSeg, for efficient and automatic brain tumour segmentation based on deep learning methodologies for aiding clinical brain research. Methods Our developed toolkit consists of three main components. First, Slicer-DeepSeg extends the 3D Slicer application and thus provides support for multiple data input/ output data formats and 3D visualization libraries. Second, Slicer core modules offer powerful image processing and analysis utilities. Third, the Slicer-DeepSeg extension provides a customized GUI for brain tumour segmentation using deep learning-based methods. Results The developed Slicer-DeepSeg was validated using a public dataset of high-grade glioma patients. The results showed that our proposed platform’s performance considerably outperforms other 3D Slicer cloud-based approaches. Conclusions Developed Slicer-DeepSeg allows the development of novel AI-assisted medical applications in neurosurgery. Moreover, it can enhance the outcomes of computer-aided diagnosis of brain tumours. Open-source Slicer-DeepSeg is available at github.com/razeineldin/Slicer-DeepSeg

Volumetric CT-based segmentation of NSCLC using 3D-Slicer

Accurate volumetric assessment in non-small cell lung cancer (NSCLC) is critical for adequately informing treatments. In this study we assessed the clinical relevance of a semiautomatic computed tomography (CT)-based segmentation method using the competitive region-growing based algorithm, implemented in the free and public available 3D-Slicer software platform. We compared the 3D-Slicer segmented volumes by three independent observers, who segmented the primary tumour of 20 NSCLC patients twice, to manual slice-by-slice delineations of five physicians. Furthermore, we compared all tumour contours to the macroscopic diameter of the tumour in pathology, considered as the “gold standard”. The 3D-Slicer segmented volumes demonstrated high agreement (overlap fractions > 0.90), lower volume variability (p = 0.0003) and smaller uncertainty areas (p = 0.0002), compared to manual slice-by-slice delineations. Furthermore, 3D-Slicer segmentations showed a strong correlation to pathology (r = 0.89, 95%CI, 0.81–0.94). Our results show that semiautomatic 3D-Slicer segmentations can be used for accurate contouring and are more stable than manual delineations. Therefore, 3D-Slicer can be employed as a starting point for treatment decisions or for high-throughput data mining research, such as Radiomics, where manual delineating often represent a time-consuming bottleneck

Robust Radiomics Feature Quantification Using Semiautomatic Volumetric Segmentation

Due to advances in the acquisition and analysis of medical imaging, it is currently possible to quantify the tumor phenotype. The emerging field of Radiomics addresses this issue by converting medical images into minable data by extracting a large number of quantitative imaging features. One of the main challenges of Radiomics is tumor segmentation. Where manual delineation is time consuming and prone to inter-observer variability, it has been shown that semi-automated approaches are fast and reduce inter-observer variability. In this study, a semiautomatic region growing volumetric segmentation algorithm, implemented in the free and publicly available 3D-Slicer platform, was investigated in terms of its robustness for quantitative imaging feature extraction. Fifty-six 3D-radiomic features, quantifying phenotypic differences based on tumor intensity, shape and texture, were extracted from the computed tomography images of twenty lung cancer patients. These radiomic features were derived from the 3D-tumor volumes defined by three independent observers twice using 3D-Slicer, and compared to manual slice-by-slice delineations of five independent physicians in terms of intra-class correlation coefficient (ICC) and feature range. Radiomic features extracted from 3D-Slicer segmentations had significantly higher reproducibility (ICC = 0.85±0.15, p = 0.0009) compared to the features extracted from the manual segmentations (ICC = 0.77±0.17). Furthermore, we found that features extracted from 3D-Slicer segmentations were more robust, as the range was significantly smaller across observers (p = 3.819e-07), and overlapping with the feature ranges extracted from manual contouring (boundary lower: p = 0.007, higher: p = 5.863e-06). Our results show that 3D-Slicer segmented tumor volumes provide a better alternative to the manual delineation for feature quantification, as they yield more reproducible imaging descriptors. Therefore, 3D-Slicer can be employed for quantitative image feature extraction and image data mining research in large patient cohorts

Fan-Slicer: A Pycuda Package for Fast Reslicing of Ultrasound Shaped Planes

Fan-Slicer (https://github.com/UCL/fan-slicer) is a Python package that enables the fast sampling (slicing) of 2D ultrasound-shaped images from a 3D volume. To increase sampling speed, CUDA kernel functions are used in conjunction with the Pycuda package. The main features include functions to generate images from both 3D surface models and 3D volumes. Additionally, the package also allows for the sampling of images from curvilinear (fan shaped planes) and linear (rectangle shaped planes) ultrasound transducers. Potential uses of Fan-slicer include the generation of large datasets of 2D images from 3D volumes and the simulation of intra-operative data among others

Group-Slicer: A collaborative extension of 3D-Slicer

AbstractIn this paper, we describe a first step towards a collaborative extension of the well-known 3D-Slicer; this platform is nowadays used as a standalone tool for both surgical planning and medical intervention. We show how this tool can be easily modified to make it collaborative so that it may constitute an integrated environment for expertise exchange as well as a useful tool for academic purposes

The SPIFFI image slicer: Revival of image slicing with plane mirrors

SPIFFI (SPectrometer for Infrared Faint Field Imaging) is the integral field spectrograph of the VLT-instrument SINFONI (SINgle Far Object Near-infrared Investigation). SINFONI is the combination of SPIFFI with the ESO adaptive optics system MACAO (Multiple Application Concept for Adaptive Optics) offering for the first time adaptive optics assisted near infrared integral field spectroscopy at an 8m-telescope. SPIFFI works in the wavelength ranger from 1.1 to 2.5 micron with a spectral resolving power ranging from R=2000 to 4500. Pixel scale ranges from 0.25 to 0.025 seconds of arc. The SPIFFI field-of-view consists of 32x32 pixels which are rearranged with an image slicer to a form a long slit. Based on the 3D slicer concept with plane mirrors, an enhanced image slicer was developed. The SPIFFI image slicer consists of two sets of mirrors, called the 'small' and the 'large' slicer. The small slicer cuts a square field of view into 32 slitlets, each of which is 32 pixels long. The large slicer rearranges the 32 slitlets into a 1024 pixels long slit. The modifications to the 3D slicer concept affect the angles of the plane mirrors of small and large slicer and lead to an improved slit geometry with very little light losses. At a mirror width of 0.3mm the light loss is <5%. All reflective surfaces are flat and can be manufactured with a high surface quality. This is especially important for the adaptive optics mode of SINFONI. We explain the concept of the SPIFFI mirror slicer and describe details of the manufacturing process.Comment: 7 pages, 4 figures, to appear in SPIE proceedings 'Astronomical Telescopes and Instrumentation 2000

A Scalable, Chunk-based Slicer for Cooperative 3D Printing

Cooperative 3D printing is an emerging technology that aims to increase the 3D printing speed and to overcome the size limit of the printable object by having multiple mobile 3D printers (printhead-carrying mobile robots) work together on a single print job on a factory floor. It differs from traditional layer-by-layer 3D printing due to requiring multiple mobile printers to work simultaneously without interfering with each other. Therefore, a new approach for slicing a digital model and generating commands for the mobile printers is needed, which has not been discussed in literature before. We propose a chunk-by-chunk based slicer that divides an object into chunks so that different mobile printers can print different chunks simultaneously without interfering with each other. In this paper, we first developed a slicer for cooperative 3D printing with two mobile fused deposition modeling (FDM) printers. To enable many more mobile printers working together, we then developed a framework for scaling to many mobile printers with high parallel efficiency. To validate our slicer for the cooperative 3D printing process, we have also developed a simulator environment, which can be a valuable tool in visualizing and optimizing a cooperative 3D printing strategy. This simulation environment was also developed to export the visualization in a generic format for use elsewhere. Results show that the developed slicer and simulator are working effectively

Segmetation of tomographic data in 3D Slicer

Táto práca obsahuje základný teoretický rozbor segmentácie obrazu pomocou techniky SVM, teóriu klasifikácie dát a popis programu 3D Slicer. Práca popisuje spracovanie medicínskych obrazov a demonštruje problematiku segmentácie týchto obrazov. Obsahuje návrh a implementáciu metódy SVM v programe 3D Slicer ako rozširovací modul programu. SVM metóda je porovnaná s jednoduchými segmentačnými metódami programu 3D Slicer. Experimentálne je overená kvalita segmentácie metódou SVM na reálnych subjektoch.This thesis contains basic theoretical information about SVM-based image segmentation and data classification. Basic information about 3D Slicer software are presented. Aspects of medical images segmentation are described. Workplan and implemetation of SVM method for MRI segmentation in 3D Slicer sofware as extension module is created. SVM method is compared with simple segmentation algorithms included in 3D Slicer. Quality of segmentation, based on SVM, tested on real subjects is experimentaly demonstrated.

Development environment construction of medical imaging software 3d slicer

3D Slicer is an open source software platform for medical image informatics, image processing and 3D visualization. Due to different medical functional requirements, a 3D slicer tailored to the doctor's needs is a great choice for people. However, the development of open source software has some difficulties in environment construction and other aspects. In this paper, after repeated attempts, literature review and research, GitHub download, CMAKE compilation and VS generation of the appropriate version were found. It can quickly build a 3D Slicer programming environment, which is of great significance for the subsequent modification and development of 3D Slicer