Technical note: Extension of CERR for computational radiomics: a comprehensive MATLAB platform for reproducible radiomics research

PurposeRadiomics is a growing field of image quantitation, but it lacks stable and high-quality software systems. We extended the capabilities of the Computational Environment for Radiological Research (CERR) to create a comprehensive, open-source, MATLAB-based software platform with an emphasis on reproducibility, speed, and clinical integration of radiomics research. MethodThe radiomics tools in CERR were designed specifically to quantitate medical images in combination with CERR's core functionalities of radiological data import, transformation, management, image segmentation, and visualization. CERR allows for batch calculation and visualization of radiomics features, and provides a user-friendly data structure for radiomics metadata. All radiomics computations are vectorized for speed. Additionally, a test suite is provided for reconstruction and comparison with radiomics features computed using other software platforms such as the Insight Toolkit (ITK) and PyRadiomics. CERR was evaluated according to the standards defined by the Image Biomarker Standardization Initiative. CERR's radiomics feature calculation was integrated with the clinically used MIM software using its MATLAB((R)) application programming interface. ResultsThe CERR provides a comprehensive computational platform for radiomics analysis. Matrix formulations for the compute-intensive Haralick texture resulted in speeds that are superior to the implementation in ITK 4.12. For an image discretized into 32 bins, CERR achieved a speedup of 3.5 times over ITK. The CERR test suite enabled the successful identification of programming errors as well as genuine differences in radiomics definitions and calculations across the software packages tested. ConclusionThe CERR's radiomics capabilities are comprehensive, open-source, and fast, making it an attractive platform for developing and exploring radiomics signatures across institutions. The ability to both choose from a wide variety of radiomics implementations and to integrate with a clinical workflow makes CERR useful for retrospective as well as prospective research analyses

I Simulatori in realtà virtuale: un ausilio nella formazione chirurgica

Negli ultimi anni la necessità di formazione in campo laparoscopico ha spinto verso la creazione di simulatori chirurgici di diversa fattura e diversa complessità. Al momento molti di questi sono disponibili in commercio. Ognuna di questi ha il proprio design, struttura e programma di formazione. L'evoluzione è rappresentata dall’utilizzo della Realtà Virtuale, che mima l'azione reale e lavora sulle diverse competenze acquisite durante i corsi di formazione e l’esperienza chirurgica al campo operatorio. Il ruolo della formazione "sicura ed efficiente" è necessario nel corso di una specializzazione in chirurgia e durante la formazione continua. La simulazione in realtà virtuale è in grado di offrire un numero infinito di scenari chirurgici. I simulatori chirurgici in realtà virtuale di ultima generazione sono forniti di percorsi di formazione graduali che guidano lo specializzando nell’acquisizione di manualità “fine” nei singoli tasks fino alla procedura completa “full task” di un intervento chirurgico, ad esempio una colecistectomia. In questo studio abbiamo voluto testare la validità di un’acquisizione graduale di tecnica manuale “step by step” rispetto all’esercizio diretto solo su una procedura completa mediante l’ausilio di un simulatore in Virtual Reality, il LapMentor®(Simbionix,Israele). Specializzandi in Chirurgia Generale privi di esperienza precedente in laparoscopia hanno ottenuto risultati migliori sulla procedura completa della colecistectomia laparoscopica procedendo durante il corso step by step rispetto a coloro che hanno eseguito la procedura completa “full task” direttamente. Il nostro studio conferma che una buona esperienza e la conoscenza delle capacità tecniche di base nel campo della formazione laparoscopica migliorano le prestazioni nella procedura completa.In the last years the need for training in laparoscopy has led to the creation of surgical simulators of varying complexity and different bill. Currently, many of these are commercially available. Each of these has its own design, structure and training program. The trend is the use of virtual reality, which mimics the real action and work on various skills acquired during the training and experience in the surgical operating field. The role of training on safe and efficient "is necessary in the course of specialization in surgery and during the training. The simulation in virtual reality is able to offer an infinite number of surgical scenarios. The surgical simulators in virtual reality are equipped with the latest training courses that guide the gradual specializing in the acquisition of manual skills "end" in the individual tasks to complete procedure "full task" for surgery, such as a cholecystectomy. In this study we wanted to test the validity of the gradual acquisition of technical manual "step by step" only on a direct comparison with the whole procedure with the help of a mortgage in Virtual Reality, the LapMentor ® (Simbionix, Israel) .Specializing in general surgery with no previous experience in laparoscopy have performed better on the whole procedure of laparoscopic cholecystectomy during the course of proceeding step by step than those who performed the procedure complete "full task" directly. Our study confirms that a good experience and knowledge of basic technical skills in training laparoscopic improve performance in the whole procedure

FPGA architecture for fast parallel computation of co-occurrence matrices

This paper presents a novel architecture for fast parallel computation of co-occurrence matrices in high throughput image analysis applications for which time performance is critical. The architecture was implemented on a Xilinx Virtex-XCV2000E-6 FPGA using VHDL. The symmetry and sparseness of the co-occurrence matrices are exploited to achieve improved processing times, and smaller, flexible area utilization as compared with the state of the art. The performance of the proposed architecture is evaluated using input images of various dimensions, in comparison with an optimized software implementation running on a conventional general purpose processor. Simulations of the architecture on contemporary FPGA devices show that it can deliver a speedup of two orders of magnitude over software. © 2006 Elsevier B.V. All rights reserved

Abstract FPGA architecture for fast parallel computation of co-occurrence matrices

This paper presents a novel architecture for fast parallel computation of co-occurrence matrices in high throughput image analysis applications for which time performance is critical. The architecture was implemented on a Xilinx Virtex-XCV2000E-6 FPGA using VHDL. The symmetry and sparseness of the co-occurrence matrices are exploited to achieve improved processing times, and smaller, flexible area utilization as compared with the state of the art. The performance of the proposed architecture is evaluated using input images of various dimensions, in comparison with an optimized software implementation running on a conventional general purpose processor. Simulations of the architecture on contemporary FPGA devices show that it can deliver a speedup of two orders of magnitude over software. Ó 2006 Elsevier B.V. All rights reserved