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

João Ramalhinho, Thomas Dowrick, Bonmati Coll, E. and Matthew J. Clarkson 2023. Fan-Slicer: A Pycuda Package for Fast Reslicing of Ultrasound Shaped Planes. Journal of Open Research Software. 11 (1), p. 3. https://doi.org/10.5334/jors.422

TitleFan-Slicer: A Pycuda Package for Fast Reslicing of Ultrasound Shaped Planes
TypeJournal article
AuthorsJoão Ramalhinho, Thomas Dowrick, Bonmati Coll, E. and Matthew J. Clarkson
Abstract

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.

JournalJournal of Open Research Software
Journal citation11 (1), p. 3
ISSN2049-9647
Year2023
PublisherUbiquity Press
Publisher's version
License
CC BY 4.0
File Access Level
Open (open metadata and files)
Digital Object Identifier (DOI)https://doi.org/10.5334/jors.422
Web address (URL)https://doi.org/10.5334/jors.422
Publication dates
Published08 Feb 2023

Related outputs

Active learning using adaptable task-based prioritisation
Shaheer U. Saeed, João Ramalhinho, Mark Pinnock, Ziyi Shen, Yunguan Fu, Nina Montaña-Brown, Ester Bonmati, Dean C. Barratt, Stephen P. Pereira, Brian Davidson, Matthew J. Clarkson and Yipeng Hu 2024. Active learning using adaptable task-based prioritisation. Medical Image Analysis. 95 103181. https://doi.org/10.1016/j.media.2024.103181

Voice-Assisted Image Labeling for Endoscopic Ultrasound Classification Using Neural Networks
Bonmati Coll, E., Hu, Y., Grimwood, A., Johnson, G.J., Goodchild, G., Keane, M.G., Gurusamy, K., Davidson, B., Clarkson, M.J., Pereira, S.P. and Barratt, D.C. 2022. Voice-Assisted Image Labeling for Endoscopic Ultrasound Classification Using Neural Networks. IEEE Transactions on Medical Imaging. 41 (6), pp. 1311-1319. https://doi.org/10.1109/tmi.2021.3139023

Assisted Probe Positioning for Ultrasound Guided Radiotherapy Using Image Sequence Classification
Grimwood, A., McNair, H., Hu, Y., Bonmati Coll, E., Barratt, D. and Harris, E.J. 2020. Assisted Probe Positioning for Ultrasound Guided Radiotherapy Using Image Sequence Classification. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 23rd International Conference. Lima, Peru 04 - 08 Oct 2020 Springer. https://doi.org/10.1007/978-3-030-59716-0_52

Novel Brain Complexity Measures Based on Information Theory
Bonmati Coll, E., Bardera, A., Feixas, M. and Boada, I. 2018. Novel Brain Complexity Measures Based on Information Theory. Entropy. 20 (7) 491. https://doi.org/10.3390/e20070491

Determination of optimal ultrasound planes for the initialisation of image registration during endoscopic ultrasound-guided procedures
Bonmati Coll, E., Hu, Y., Gibson, E., Uribarri, L., Keane, G., Gurusami, K., Davidson, B., Pereira, S.P., Clarkson, M.J. and Barratt, D.C. 2018. Determination of optimal ultrasound planes for the initialisation of image registration during endoscopic ultrasound-guided procedures. International Journal of Computer Assisted Radiology and Surgery. 13, pp. 875-883. https://doi.org/10.1007/s11548-018-1762-2

Automatic Multi-Organ Segmentation on Abdominal CT with Dense V-Networks
Gibson, E., Giganti, F., Hu, Y., Bonmati Coll, E., Bandula, S., Gurusamy, K., Davidson, B., Pereira, S.P., Clarkson, M.J. and Barratt, D.C. 2018. Automatic Multi-Organ Segmentation on Abdominal CT with Dense V-Networks. IEEE Transactions on Medical Imaging. 37 (8), pp. 1822-1834. https://doi.org/10.1109/tmi.2018.2806309

Automatic segmentation method of pelvic floor levator hiatus in ultrasound using a self-normalizing neural network
Bonmati Coll, E., Hu, Y., Sindhwani, N., Dietz, H.P., D'hooge, J., Barratt, D., Deprest, J. and Vercauteren, T. 2018. Automatic segmentation method of pelvic floor levator hiatus in ultrasound using a self-normalizing neural network. Journal of Medical Imaging. 5 (2) 021206. https://doi.org/10.1117/1.jmi.5.2.021206

Brain parcellation based on information theory
Bonmati Coll, E., Bardera, A. and Boada, I. 2017. Brain parcellation based on information theory. Computer Methods and Programs in Biomedicine. 151, pp. 203-212. https://doi.org/10.1016/j.cmpb.2017.07.012

Towards image-guided pancreas and biliary endoscopy: Automatic multi-organ segmentation on abdominal CT with dense dilated networks
Gibson, E., Giganti, F., Hu, Y., Bonmati Coll, E., Bandula, S., Gurusamy, K., Davidson, B.R., Pereira, S.P., Clarkson, M.J. and Barratt, D.C. 2017. Towards image-guided pancreas and biliary endoscopy: Automatic multi-organ segmentation on abdominal CT with dense dilated networks. Medical Image Computing and Computer Assisted Intervention − MICCAI 2017. Quebec City, QC, Canada 11 - 13 Sep 2017 Springer. https://doi.org/10.1007/978-3-319-66182-7_83

2D-3D Registration Accuracy Estimation for Optimised Planning of Image-Guided Pancreatobiliary Interventions
Hu, Y., Bonmati Coll, E., Gibson, E., Hipwell, J.H., Hawkes, D.J., Bandula, S., Pereira, S.P. and Barratt, D.C. 2016. 2D-3D Registration Accuracy Estimation for Optimised Planning of Image-Guided Pancreatobiliary Interventions. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016. MICCAI 2016. Athens, Greece 17 - 21 Oct 2016 Springer. https://doi.org/10.1007/978-3-319-46720-7_60

Assessment of Electromagnetic Tracking Accuracy for Endoscopic Ultrasound
Bonmati Coll, E., Hu, Y., Gurusamy, K., Davidson, B., Pereira, S.P., Clarkson, M.J. and Barratt, D.C. 2016. Assessment of Electromagnetic Tracking Accuracy for Endoscopic Ultrasound. Computer-Assisted and Robotic Endoscopy. CARE 2016. Athens, Greece 17 Oct 2016 Springer. https://doi.org/10.1007/978-3-319-54057-3_4

Measuring Complex Brain Networks Structure
Bonmati Ester, Bardera Anton, Boada Imma and Bonmati Coll, E. 2016. Measuring Complex Brain Networks Structure. Frontiers in Neuroinformatics. Conference Abstract: Neuroinformatics 2016. https://doi.org/10.3389/conf.fninf.2016.20.00012

Hierarchical clustering based on the information bottleneck method using a control process
Bonmati Coll, E., Bardera, A., Boada, I., Feixas, M. and Sbert, M. 2015. Hierarchical clustering based on the information bottleneck method using a control process. Pattern Analysis and Applications (PAA). 18, pp. 619-637. https://doi.org/10.1007/s10044-015-0467-1

Permalink - https://westminsterresearch.westminster.ac.uk/item/w16zq/fan-slicer-a-pycuda-package-for-fast-reslicing-of-ultrasound-shaped-planes


Share this

Usage statistics

128 total views
57 total downloads
These values cover views and downloads from WestminsterResearch and are for the period from September 2nd 2018, when this repository was created.