Retrieval and Registration of Long-Range Overlapping Frames for Scalable Mosaicking of In Vivo Fetoscopy

Purpose: The standard clinical treatment of Twin-to-Twin Transfusion Syndrome consists in the photo-coagulation of undesired anastomoses located on the placenta which are responsible to a blood transfer between the two twins. While being the standard of care procedure, fetoscopy suffers from a limited field-of-view of the placenta resulting in missed anastomoses. To facilitate the task of the clinician, building a global map of the placenta providing a larger overview of the vascular network is highly desired. Methods: To overcome the challenging visual conditions inherent to in vivo sequences (low contrast, obstructions or presence of artifacts, among others), we propose the following contributions: (i) robust pairwise registration is achieved by aligning the orientation of the image gradients, and (ii) difficulties regarding long-range consistency (e.g. due to the presence of outliers) is tackled via a bag-of-word strategy, which identifies overlapping frames of the sequence to be registered regardless of their respective location in time. Results: In addition to visual difficulties, in vivo sequences are characterised by the intrinsic absence of gold standard. We present mosaics motivating qualitatively our methodological choices and demonstrating their promising aspect. We also demonstrate semi-quantitatively, via visual inspection of registration results, the efficacy of our registration approach in comparison to two standard baselines. Conclusion: This paper proposes the first approach for the construction of mosaics of placenta in in vivo fetoscopy sequences. Robustness to visual challenges during registration and long-range temporal consistency are proposed, offering first positive results on in vivo data for which standard mosaicking techniques are not applicable.Comment: Accepted for publication in International Journal of Computer Assisted Radiology and Surgery (IJCARS

Effective deep learning training for single-image super-resolution in endomicroscopy exploiting video-registration-based reconstruction

Purpose: Probe-based Confocal Laser Endomicroscopy (pCLE) is a recent imaging modality that allows performing in vivo optical biopsies. The design of pCLE hardware, and its reliance on an optical fibre bundle, fundamentally limits the image quality with a few tens of thousands fibres, each acting as the equivalent of a single-pixel detector, assembled into a single fibre bundle. Video-registration techniques can be used to estimate high-resolution (HR) images by exploiting the temporal information contained in a sequence of low-resolution (LR) images. However, the alignment of LR frames, required for the fusion, is computationally demanding and prone to artefacts. Methods: In this work, we propose a novel synthetic data generation approach to train exemplar-based Deep Neural Networks (DNNs). HR pCLE images with enhanced quality are recovered by the models trained on pairs of estimated HR images (generated by the video-registration algorithm) and realistic synthetic LR images. Performance of three different state-of-the-art DNNs techniques were analysed on a Smart Atlas database of 8806 images from 238 pCLE video sequences. The results were validated through an extensive Image Quality Assessment (IQA) that takes into account different quality scores, including a Mean Opinion Score (MOS). Results: Results indicate that the proposed solution produces an effective improvement in the quality of the obtained reconstructed image. Conclusion: The proposed training strategy and associated DNNs allows us to perform convincing super-resolution of pCLE images

dzhoshkun/github-mirror-test: My awesome 2.5 version

A dummy release to test migrating repo from GitLa

Bruker2nifti: Magnetic Resonance Images converter from Bruker

In clinical and pre-clinical research involving medical images, the first step following a Magnetic Resonance Imaging dataset acquisition, usually entails the conversion of image data from the native scanner format to a format suitable for the intended analysis. The proprietary Bruker ParaVision software currently does not provide the tools for conversion of the data to suitable and open formats for research, such as nifti (Cox, Robert W and Ashburner, John and Breman, Hester and Fissell, Kate and Haselgrove, Christian and Holmes, Colin J and Lancaster, Jack L and Rex, David E and Smith, Stephen M and Woodward, Jeffrey B and others 2004), for which most of the available tools for medical image analysis are implemented. For this purpose we have designed and developed bruker2nifti, a pip-installable Python tool provided with a Graphical User Interface to convert from the native MRI Bruker format to the nifti format, without any intermediate step through the DICOM standard formats (Mildenberger, Eichelberg, and Martin 2002). Bruker2nifti is intended to be a tool to access the data structure and to parse all parameter files of the Bruker ParaVision format into python dictionaries, to select the relevant information to fill the Nifti header and data volume. Lastly it is meant to be a starting point where to integrate possible future variations in Bruker hardware and ParaVision software future releases.status: accepte

bruker2nifti

The .zip file contains the repository bruker2nifti as submitted to JOSS, Journal of Open Source Software ------ Description ----- Bruker2nifti is an open source medical image format converter from raw Bruker ParaVision to NifTi, without any intermediate step through the DICOM standard formats. Bruker2nifti is a pip-installable Python tool provided with a Graphical User Interface and a Command Line Utility to access the conversion method

Retrieval and registration of long-range overlapping frames for scalable mosaicking of in vivo fetoscopy

The standard clinical treatment of Twin-to-Twin transfusion syndrome consists in the photo-coagulation of undesired anastomoses located on the placenta which are responsible to a blood transfer between the two twins. While being the standard of care procedure, fetoscopy suffers from a limited field-of-view of the placenta resulting in missed anastomoses. To facilitate the task of the clinician, building a global map of the placenta providing a larger overview of the vascular network is highly desired.status: accepte