A shape-constraint adversarial framework with instance-normalized spatio-temporal features for inter-fetal membrane segmentation
Alessandro Casella,Alessandro Casella,Sara Moccia,Dario Paladini,Emanuele Frontoni,Elena De Momi,Leonardo S. Mattos +6 more
TLDR
In this paper, a new deep learning framework for inter-fetal membrane segmentation on in-vivo fetoscopic videos is presented, which enhances existing architectures by encoding a novel (instance-normalized) dense block, invariant to illumination changes, that extracts spatio-temporal features to enforce pixel connectivity in time, and relying on an adversarial training, which constrains macro appearance.About:
This article is published in Medical Image Analysis.The article was published on 2021-02-19 and is currently open access. It has received 9 citations till now.read more
Citations
More filters
Journal ArticleDOI
A Review on Deep-Learning Algorithms for Fetal Ultrasound-Image Analysis
Maria Chiara Fiorentino,Francesca Pia Villani,Mariachiara Di Cosmo,Emanuele Frontoni,Sara Moccia +4 more
TL;DR: A detailed survey of the most recent work in the field can be found in this paper , with a total of 145 research papers published after 2017 and each paper is analyzed and commented on from both the methodology and application perspective.
Journal ArticleDOI
Artificial intelligence in the diagnosis of necrotising enterocolitis in newborns
Arkadiusz Sitek,Joanna Seliga-Siwecka,Szymon Płotka,Michał K. Grzeszczyk,Szymon Seliga,Krzysztof Wlodarczyk,Renata Bokiniec +6 more
TL;DR: A literature search on the use of AI in the diagnosis of NEC yielded 118 publications that were reduced to 8 after screening and checking for eligibility, and most publications showed promising results but no publications with evident clinical benefits were found.
Journal ArticleDOI
A deep learning approach to median nerve evaluation in ultrasound images of carpal tunnel inlet
Mariachiara Di Cosmo,Maria Chiara Fiorentino,Francesca Pia Villani,Emanuele Frontoni,Gianluca Smerilli,Emilio Filippucci,Sara Moccia +6 more
TL;DR: Wang et al. as discussed by the authors used Mask R-CNN with two additional transposed layers at segmentation head to accurately segment the median nerve directly on transverse US images, which achieved good performances both in median nerve detection and segmentation: Precision (Prec), Recall (Rec), Mean Average Precision (mAP) and Dice Similarity Coefficient (DSC).
Journal ArticleDOI
FUN-SIS: a Fully UNsupervised approach for Surgical Instrument Segmentation
TL;DR: In this paper , a fully-unsupervised approach for binary Surgical Instrument Segmentation is proposed, which uses shape-priors as realistic segmentation masks of the instruments, not necessarily coming from the same dataset/domain as the videos.
Journal ArticleDOI
Computer‐assisted fetal laser surgery in the treatment of twin‐to‐twin transfusion syndrome: Recent trends and prospects
TL;DR: This review uncovers the literature on computer‐assisted software solutions focused on TTTS and evaluates the current maturity of technologies by the technology readiness level and enumerates the necessary aspects to bring these new technologies to clinical practice.
References
More filters
Book ChapterDOI
Deep Sequential Mosaicking of Fetoscopic Videos
Sophia Bano,Francisco de Assis Guedes de Vasconcelos,Marcel Tella Amo,George Dwyer,Caspar Gruijthuijsen,Jan Deprest,Sebastien Ourselin,Emmanuel Vander Poorten,Tom Vercauteren,Danail Stoyanov +9 more
TL;DR: In this paper, a new generalized Deep Sequential Mosaicking (DSM) framework is presented for fetoscopic videos captured from different settings such as simulation, phantom, and real environments.
Book ChapterDOI
Supervised CNN Strategies for Optical Image Segmentation and Classification in Interventional Medicine
TL;DR: An overview of some of the most recent approaches (up to 2018) in the field of interventional-image analysis, with a focus on Convolutional Neural Networks (CNNs) for both segmentation and classification tasks.
Journal ArticleDOI
Deep Q-CapsNet Reinforcement Learning Framework for Intrauterine Cavity Segmentation in TTTS Fetal Surgery Planning
Jordina Torrents-Barrena,Gemma Piella,Eduard Gratacós,Elisenda Eixarch,Mario Ceresa,Miguel A. Gonalez Ballester +5 more
TL;DR: This work designs the first automatic approach to detect and segment the intrauterine cavity from axial, sagittal and coronal MRI stacks, and relies on the ability of capsule networks to successfully capture the part-whole interdependency of objects in the scene.