Image Compositing for Segmentation of Surgical Tools without Manual Annotations

We have recently published the paper "Garcia-Peraza-Herrera, L. C., Fidon, L., DEttorre, C. Stoyanov, D., Vercauteren, T., Ourselin, S. (2021). Image Compositing for Segmentation of Surgical Tools without Manual Annotations. *Transactions in Medical Imaging* ([πŸ“–](https://doi.org/10.1109/TMI.2021.3057884))". Inspired by special effects, we introduce a novel deep-learning method to segment surgical instruments in endoscopic images.

MONAI (Medical Open Network for AI): PyTorch for medical imaging

We contribute to MONAI, a PyTorch-based, open-source framework for deep learning in healthcare imaging, part of PyTorch Ecosystem.

Learning joint Segmentation of Tissues And Brain Lesions (jSTABL) from task-specific hetero-modal domain-shifted datasets

Open source PyTorch implementation of "Dorent, R., Booth, T., Li, W., Sudre, C. H., Kafiabadi, S., Cardoso, J., ... & Vercauteren, T. (2020). Learning joint segmentation of tissues and brain lesions from task-specific hetero-modal domain-shifted datasets. *Medical Image Analysis*, 67, 101862 ([πŸ“–](https://doi.org/10.1016/j.media.2020.101862))."

DeepReg: Medical image registration using deep learning

DeepReg is a freely available, community-supported open-source toolkit for research and education in medical image registration using deep learning ([πŸ“–](https://doi.org/10.21105/joss.02705)).

Intrapapillary Capillary Loop (IPCL) Classification

We provide open source code and open access data for our paper "GarcΓ­a-Peraza-Herrera, L. C., Everson, M., Lovat, L., Wang, H. P., Wang, W. L., Haidry, R., ... & Vercauteren, T. (2020). Intrapapillary capillary loop classification in magnification endoscopy: Open dataset and baseline methodology. *International journal of computer assisted radiology and surgery*, 1-9 ([πŸ“–](https://dx.doi.org/10.1007%2Fs11548-020-02127-w))."

Fetal brain MRI reconstruction (NiftyMIC)

NiftyMIC is a Python-based open-source toolkit for research developed within the GIFT-Surg project to reconstruct an isotropic, high-resolution volume from multiple, possibly motion-corrupted, stacks of low-resolution 2D slices. Read "Ebner, M., Wang, G., Li, W., Aertsen, M., Patel, P. A., Aughwane, R., ... & David, A. L. (2020). An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI. *NeuroImage*, 206, 116324 ([πŸ“–](https://doi.org/10.1016/j.neuroimage.2019.116324))."