6 research outputs found
Online Super-Resolution For Fibre-Bundle-Based Confocal Laser Endomicroscopy
Probe-based Confocal Laser Endomicroscopy (pCLE) produces microscopic images enabling real-time in vivo optical biopsy. However, the miniaturisation of the optical hardware, specifically the reliance on an optical fibre bundle as an imaging guide, fundamentally limits image quality by producing artefacts, noise, and relatively low contrast and resolution. The reconstruction approaches in clinical pCLE products do not fully alleviate these problems. Consequently, image quality remains a barrier that curbs the full potential of pCLE. Enhancing the image quality of pCLE in real-time remains a challenge. The research in this thesis is a response to this need. I have developed dedicated online super-resolution methods that account for the physics of the image acquisition process. These methods have the potential to replace existing reconstruction algorithms without interfering with the fibre design or the hardware of the device. In this thesis, novel processing pipelines are proposed for enhancing the image quality of pCLE. First, I explored a learning-based super-resolution method that relies on mapping from the low to the high-resolution space. Due to the lack of high-resolution pCLE, I proposed to simulate high-resolution data and use it as a ground truth model that is based on the pCLE acquisition physics. However, pCLE images are reconstructed from irregularly distributed fibre signals, and grid-based Convolutional Neural Networks are not designed to take irregular data as input. To alleviate this problem, I designed a new trainable layer that embeds Nadaraya- Watson regression. Finally, I proposed a novel blind super-resolution approach by deploying unsupervised zero-shot learning accompanied by a down-sampling kernel crafted for pCLE. I evaluated these new methods in two ways: a robust image quality assessment and a perceptual quality test assessed by clinical experts. The results demonstrate that the proposed super-resolution pipelines are superior to the current reconstruction algorithm in terms of image quality and clinician preference
Adversarial training with cycle consistency for unsupervised super-resolution in endomicroscopy
In recent years, endomicroscopy has become increasingly used for diagnostic
purposes and interventional guidance. It can provide intraoperative aids for
real-time tissue characterization and can help to perform visual investigations
aimed for example to discover epithelial cancers. Due to physical constraints
on the acquisition process, endomicroscopy images, still today have a low
number of informative pixels which hampers their quality. Post-processing
techniques, such as Super-Resolution (SR), are a potential solution to increase
the quality of these images. SR techniques are often supervised, requiring
aligned pairs of low-resolution (LR) and high-resolution (HR) images patches to
train a model. However, in our domain, the lack of HR images hinders the
collection of such pairs and makes supervised training unsuitable. For this
reason, we propose an unsupervised SR framework based on an adversarial deep
neural network with a physically-inspired cycle consistency, designed to impose
some acquisition properties on the super-resolved images. Our framework can
exploit HR images, regardless of the domain where they are coming from, to
transfer the quality of the HR images to the initial LR images. This property
can be particularly useful in all situations where pairs of LR/HR are not
available during the training. Our quantitative analysis, validated using a
database of 238 endomicroscopy video sequences from 143 patients, shows the
ability of the pipeline to produce convincing super-resolved images. A Mean
Opinion Score (MOS) study also confirms this quantitative image quality
assessment.Comment: Accepted for publication on Medical Image Analysis journa
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