9 research outputs found
Deep Sequential Mosaicking of Fetoscopic Videos
Twin-to-twin transfusion syndrome treatment requires fetoscopic laser
photocoagulation of placental vascular anastomoses to regulate blood flow to
both fetuses. Limited field-of-view (FoV) and low visual quality during
fetoscopy make it challenging to identify all vascular connections. Mosaicking
can align multiple overlapping images to generate an image with increased FoV,
however, existing techniques apply poorly to fetoscopy due to the low visual
quality, texture paucity, and hence fail in longer sequences due to the drift
accumulated over time. Deep learning techniques can facilitate in overcoming
these challenges. Therefore, we present a new generalized Deep Sequential
Mosaicking (DSM) framework for fetoscopic videos captured from different
settings such as simulation, phantom, and real environments. DSM extends an
existing deep image-based homography model to sequential data by proposing
controlled data augmentation and outlier rejection methods. Unlike existing
methods, DSM can handle visual variations due to specular highlights and
reflection across adjacent frames, hence reducing the accumulated drift. We
perform experimental validation and comparison using 5 diverse fetoscopic
videos to demonstrate the robustness of our framework.Comment: Accepted at MICCAI 201
Placental vessel-guided hybrid framework for fetoscopic mosaicking
Fetoscopic laser photocoagulation is used to treat twin-to-twin transfusion syndrome; however, this procedure is hindered because of difficulty in visualising the intraoperative surgical environment due to limited surgical field-of-view, unusual placenta position, limited manoeuvrability of the fetoscope and poor visibility due to fluid turbidity and occlusions. Fetoscopic video mosaicking can create an expanded field-of-view image of the fetoscopic intraoperative environment, which could support the surgeons in localising the vascular anastomoses during the fetoscopic procedure. However, classical handcrafted feature matching methods fail on in vivo fetoscopic videos. An existing state-of-the-art method on fetoscopic mosaicking relies on vessel presence and fails when vessels are not present in the view. We propose a vessel-guided hybrid fetoscopic mosaicking framework that mutually benefits from a placental vessel-based registration and a deep learning-based dense matching method to optimise the overall performance. A selection mechanism is implemented based on vessels’ appearance consistency and photometric error minimisation for choosing the best pairwise transformation. Using the extended fetoscopy placenta dataset, we experimentally show the robustness of the proposed framework, over the state-of-the-art methods, even in vessel-free, low-textured, or low illumination non-planar fetoscopic views
Deep Placental Vessel Segmentation for Fetoscopic Mosaicking
During fetoscopic laser photocoagulation, a treatment for twin-to-twin
transfusion syndrome (TTTS), the clinician first identifies abnormal placental
vascular connections and laser ablates them to regulate blood flow in both
fetuses. The procedure is challenging due to the mobility of the environment,
poor visibility in amniotic fluid, occasional bleeding, and limitations in the
fetoscopic field-of-view and image quality. Ideally, anastomotic placental
vessels would be automatically identified, segmented and registered to create
expanded vessel maps to guide laser ablation, however, such methods have yet to
be clinically adopted. We propose a solution utilising the U-Net architecture
for performing placental vessel segmentation in fetoscopic videos. The obtained
vessel probability maps provide sufficient cues for mosaicking alignment by
registering consecutive vessel maps using the direct intensity-based technique.
Experiments on 6 different in vivo fetoscopic videos demonstrate that the
vessel intensity-based registration outperformed image intensity-based
registration approaches showing better robustness in qualitative and
quantitative comparison. We additionally reduce drift accumulation to
negligible even for sequences with up to 400 frames and we incorporate a scheme
for quantifying drift error in the absence of the ground-truth. Our paper
provides a benchmark for fetoscopy placental vessel segmentation and
registration by contributing the first in vivo vessel segmentation and
fetoscopic videos dataset.Comment: Accepted at MICCAI 202
Computer Vision in the Surgical Operating Room
Background: Multiple types of surgical cameras are used in modern surgical practice and provide a rich visual signal that is used by surgeons to visualize the clinical site and make clinical decisions. This signal can also be used by artificial intelligence (AI) methods to provide support in identifying instruments, structures, or activities both in real-time during procedures and postoperatively for analytics and understanding of surgical processes. Summary: In this paper, we provide a succinct perspective on the use of AI and especially computer vision to power solutions for the surgical operating room (OR). The synergy between data availability and technical advances in computational power and AI methodology has led to rapid developments in the field and promising advances. Key Messages: With the increasing availability of surgical video sources and the convergence of technologiesaround video storage, processing, and understanding, we believe clinical solutions and products leveraging vision are going to become an important component of modern surgical capabilities. However, both technical and clinical challenges remain to be overcome to efficiently make use of vision-based approaches into the clinic
FetReg: Placental Vessel Segmentation and Registration in Fetoscopy Challenge Dataset
Fetoscopy laser photocoagulation is a widely used procedure for the treatment
of Twin-to-Twin Transfusion Syndrome (TTTS), that occur in mono-chorionic
multiple pregnancies due to placental vascular anastomoses. This procedure is
particularly challenging due to limited field of view, poor manoeuvrability of
the fetoscope, poor visibility due to fluid turbidity, variability in light
source, and unusual position of the placenta. This may lead to increased
procedural time and incomplete ablation, resulting in persistent TTTS.
Computer-assisted intervention may help overcome these challenges by expanding
the fetoscopic field of view through video mosaicking and providing better
visualization of the vessel network. However, the research and development in
this domain remain limited due to unavailability of high-quality data to encode
the intra- and inter-procedure variability. Through the \textit{Fetoscopic
Placental Vessel Segmentation and Registration (FetReg)} challenge, we present
a large-scale multi-centre dataset for the development of generalized and
robust semantic segmentation and video mosaicking algorithms for the fetal
environment with a focus on creating drift-free mosaics from long duration
fetoscopy videos. In this paper, we provide an overview of the FetReg dataset,
challenge tasks, evaluation metrics and baseline methods for both segmentation
and registration. Baseline methods results on the FetReg dataset shows that our
dataset poses interesting challenges, offering large opportunity for the
creation of novel methods and models through a community effort initiative
guided by the FetReg challenge
Globally Optimal Fetoscopic Mosaicking Based on Pose Graph Optimisation With Affine Constraints
Fetoscopic laser ablation surgery could be guided using a high-quality panorama of the operating site, representing a map of the placental vasculature. This can be achieved during the initial inspection phase of the procedure using image mosaicking techniques. Due to the lack of camera calibration in the operating room, it has been mostly modelled as an affine registration problem. While previous work mostly focuses on image feature extraction for visual odometry, the challenges related to large-scale reconstruction (re-localisation, loop closure, drift correction) remain largely unaddressed in this context. This letter proposes using pose graph optimisation to produce globally optimal image mosaics of placental vessels. Our approach follows the SLAM framework with a front-end for visual odometry and a back-end for long-term refinement. Our front-end uses a recent state-of-the-art odometry approach based on vessel segmentation, which is then managed by a key-frame structure and the bag-of-words (BoW) scheme to retrieve loop closures. The back-end, which is our key contribution, models odometry and loop closure constraints as a pose graph with affine warpings between states. This problem in the special Euclidean space cannot be solved by existing pose graph algorithms and available libraries such as G2O. We model states on affine Lie group with local linearisation in its Lie algebra. The cost function is established using Mahalanobis distance with the vectorisation of the Lie algebra. Finally, an iterative optimisation algorithm is adopted to minimise the cost function. The proposed pose graph optimisation is first validated on simulation data with a synthetic trajectory that has different levels of noise and different numbers of loop closures. Then the whole system is validated using real fetoscopic data that has three sequences with different numbers of frames and loop closures. Experimental results validate the advantage of the proposed method compared with baselines
FetReg2021: A Challenge on Placental Vessel Segmentation and Registration in Fetoscopy
Fetoscopy laser photocoagulation is a widely adopted procedure for treating Twin-to-Twin Transfusion Syndrome (TTTS). The procedure involves photocoagulation pathological anastomoses to regulate blood exchange among twins. The procedure is particularly challenging due to the limited field of view, poor manoeuvrability of the fetoscope, poor visibility, and variability in illumination. These challenges may lead to increased surgery time and incomplete ablation. Computer-assisted intervention (CAI) can provide surgeons with decision support and context awareness by identifying key structures in the scene and expanding the fetoscopic field of view through video mosaicking. Research in this domain has been hampered by the lack of high-quality data to design, develop and test CAI algorithms. Through the Fetoscopic Placental Vessel Segmentation and Registration (FetReg2021) challenge, which was organized as part of the MICCAI2021 Endoscopic Vision challenge, we released the first largescale multicentre TTTS dataset for the development of generalized and robust semantic segmentation and video mosaicking algorithms. For this challenge, we released a dataset of 2060 images, pixel-annotated for vessels, tool, fetus and background classes, from 18 in-vivo TTTS fetoscopy procedures and 18 short video clips. Seven teams participated in this challenge and their model performance was assessed on an unseen test dataset of 658 pixel-annotated images from 6 fetoscopic procedures and 6 short clips. The challenge provided an opportunity for creating generalized solutions for fetoscopic scene understanding and mosaicking. In this paper, we present the findings of the FetReg2021 challenge alongside reporting a detailed literature review for CAI in TTTS fetoscopy. Through this challenge, its analysis and the release of multi-centre fetoscopic data, we provide a benchmark for future research in this field
Tracking and Mapping in Medical Computer Vision: A Review
As computer vision algorithms are becoming more capable, their applications
in clinical systems will become more pervasive. These applications include
diagnostics such as colonoscopy and bronchoscopy, guiding biopsies and
minimally invasive interventions and surgery, automating instrument motion and
providing image guidance using pre-operative scans. Many of these applications
depend on the specific visual nature of medical scenes and require designing
and applying algorithms to perform in this environment.
In this review, we provide an update to the field of camera-based tracking
and scene mapping in surgery and diagnostics in medical computer vision. We
begin with describing our review process, which results in a final list of 515
papers that we cover. We then give a high-level summary of the state of the art
and provide relevant background for those who need tracking and mapping for
their clinical applications. We then review datasets provided in the field and
the clinical needs therein. Then, we delve in depth into the algorithmic side,
and summarize recent developments, which should be especially useful for
algorithm designers and to those looking to understand the capability of
off-the-shelf methods. We focus on algorithms for deformable environments while
also reviewing the essential building blocks in rigid tracking and mapping
since there is a large amount of crossover in methods. Finally, we discuss the
current state of the tracking and mapping methods along with needs for future
algorithms, needs for quantification, and the viability of clinical
applications in the field. We conclude that new methods need to be designed or
combined to support clinical applications in deformable environments, and more
focus needs to be put into collecting datasets for training and evaluation.Comment: 31 pages, 17 figure