ArticleInternational journal of computer assisted radiology and surgery2020
Deep learning-based fetoscopic mosaicking for field-of-view expansion.
Article in International journal of computer assisted radiology and surgery, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 29 citations in OpenAlex.
- Surgical Instrument Segmentation via Segment-Then-Classify Framework with Instance-Level Spatiotemporal Consistency Modeling.Journal of imaging · 2025Article
- Learning-based keypoint registration for fetoscopic mosaicking.International journal of computer assisted radiology and surgery · 2024Article
- Placental vessel segmentation and registration in fetoscopy: Literature review and MICCAI FetReg2021 challenge findings.Medical image analysis · 2024Review
- Toward a navigation framework for fetoscopy.International journal of computer assisted radiology and surgery · 2023Article
- Comparison of image registration methods for combining laparoscopic video and spectral image data.Scientific reports · 2022Article
- Computer-assisted fetal laser surgery in the treatment of twin-to-twin transfusion syndrome: Recent trends and prospects.Prenatal diagnosis · 2022Review
- Regenerative medicine: prenatal approaches.The Lancet. Child & adolescent health · 2022Review
- Amniotic Fluid Classification and Artificial Intelligence: Challenges and Opportunities.Sensors (Basel, Switzerland) · 2022Review
- Robust fetoscopic mosaicking from deep learned flow fields.International journal of computer assisted radiology and surgery · 2022Article
- Qualitative Comparison of Image Stitching Algorithms for Multi-Camera Systems in Laparoscopy.Journal of imaging · 2022Article
- Deep homography estimation in dynamic surgical scenes for laparoscopic camera motion extraction.Computer methods in biomechanics and biomedical engineering. Imaging & visualization · 2022Article
- Deep Learning in Biomedical Optics.Lasers in surgery and medicine · 2021Review
- Real-Time Nonrigid Mosaicking of Laparoscopy Images.IEEE transactions on medical imaging · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 3 institutions in 2 countries.
Funding
Abstract
purposeFetoscopic laser photocoagulation is a minimally invasive surgical procedure used to treat twin-to-twin transfusion syndrome (TTTS), which involves localization and ablation of abnormal vascular connections on the placenta to regulate the blood flow in both fetuses. This procedure is particularly challenging due to the limited field of view, poor visibility, occasional bleeding, and poor image quality. Fetoscopic mosaicking can help in creating an image with the expanded field of view which could facilitate the clinicians during the TTTS procedure.
methodsWe propose a deep learning-based mosaicking framework for diverse fetoscopic videos captured from different settings such as simulation, phantoms, ex vivo, and in vivo environments. The proposed mosaicking framework extends an existing deep image homography model to handle video data by introducing the controlled data generation and consistent homography estimation modules. Training is performed on a small subset of fetoscopic images which are independent of the testing videos.
resultsWe perform both quantitative and qualitative evaluations on 5 diverse fetoscopic videos (2400 frames) that captured different environments. To demonstrate the robustness of the proposed framework, a comparison is performed with the existing feature-based and deep image homography methods.
conclusionThe proposed mosaicking framework outperformed existing methods and generated meaningful mosaic, while reducing the accumulated drift, even in the presence of visual challenges such as specular highlights, reflection, texture paucity, and low video resolution.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.