Journal ArticleDOI
Automated Segmentation of Knee Bone and Cartilage combining Statistical Shape Knowledge and Convolutional Neural Networks: Data from the Osteoarthritis Initiative
Reads0
Chats0
TLDR
Combining localized classification via CNNs with statistical anatomical knowledge via SSMs results in a state‐of‐the‐art segmentation method for knee bones and cartilage from MRI data.About:
This article is published in Medical Image Analysis.The article was published on 2019-02-01. It has received 238 citations till now. The article focuses on the topics: Image segmentation & Segmentation.read more
Citations
More filters
Journal ArticleDOI
3D Deep Learning on Medical Images: A Review.
Satya P. Singh,Lipo Wang,Sukrit Gupta,Haveesh Goli,Parasuraman Padmanabhan,Balazs Gulyas,Balazs Gulyas +6 more
TL;DR: The history of how the 3D CNN was developed from its machine learning roots is traced, a brief mathematical description of3D CNN is provided and the preprocessing steps required for medical images before feeding them to 3DCNNs are provided.
Journal ArticleDOI
Going Deep in Medical Image Analysis: Concepts, Methods, Challenges, and Future Directions
TL;DR: In this article, a review of the recent developments in medical image analysis with deep learning can be found and a critical review of related major aspects is provided. But the authors do not assume prior knowledge of deep learning and make a significant contribution in explaining the core deep learning concepts to the non-experts in the Medical Community.
Journal ArticleDOI
VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images
Anjany Sekuboyina,Malek El Husseini,Amirhossein Bayat,Maximilian T. Löffler,Hans Liebl,Hongwei Li,Giles Tetteh,Jan Kukačka,Christian Payer,Darko Štern,Martin Urschler,Maodong Chen,Dalong Cheng,Nikolas Lessmann,Yujin Hu,Tianfu Wang,Dong Yang,Daguang Xu,Felix Ambellan,Tamaz Amiranashvili,Moritz Ehlke,Hans Lamecker,Sebastian Lehnert,Marilia Lirio,Nicolás Pérez de Olaguer,Heiko Ramm,Manish Sahu,Alexander Tack,Stefan Zachow,Tao Jiang,Xinjun Ma,Christoph Angerman,Xin Wang,Kevin W. Brown,Alexandre Kirszenberg,Elodie Puybareau,Di Chen,Yiwei Bai,Brandon H. Rapazzo,Timyoas Yeah,Amber Zhang,Shangliang Xu,Feng Hou,Zhiqiang He,Chan Zeng,Zheng Xiangshang,Xu Liming,Tucker Netherton,Raymond P. Mumme,Laurence E. Court,Zixun Huang,Chenhang He,Li-Wen Wang,Sai Ho Ling,Lê Duy Huỳnh,Nicolas Boutry,Roman Jakubicek,Jiri Chmelik,Supriti Mulay,Mohanasankar Sivaprakasam,Johannes C. Paetzold,Suprosanna Shit,Ivan Ezhov,Benedikt Wiestler,Ben Glocker,Alexander Valentinitsch,Markus Rempfler,Björn H. Menze,Jan S. Kirschke +68 more
TL;DR: The principal takeaway from VerSe: the performance of an algorithm in labelling and segmenting a spine scan hinges on its ability to correctly identify vertebrae in cases of rare anatomical variations.
Proceedings ArticleDOI
Improving Robustness of Deep Learning Based Knee MRI Segmentation: Mixup and Adversarial Domain Adaptation
TL;DR: In this article, the authors investigated two modern regularization techniques (mixup and adversarial domain adaptation) to improve the robustness of DL-based knee cartilage segmentation to new MRI acquisition settings.
Journal ArticleDOI
Machine learning in knee osteoarthritis: A review
Christos Kokkotis,Serafeim Moustakidis,Elpiniki I. Papageorgiou,Giannis Giakas,Dimitrios Tsaopoulos +4 more
TL;DR: Knee osteoarthritis is a big data problem in terms of data complexity, heterogeneity and size as it has been commonly considered in the literature and Machine Learning has attracted significant interest from the scientific community to cope with the aforementioned challenges.
References
More filters
Book ChapterDOI
U-Net: Convolutional Networks for Biomedical Image Segmentation
TL;DR: Neber et al. as discussed by the authors proposed a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently, which can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
Posted Content
U-Net: Convolutional Networks for Biomedical Image Segmentation
TL;DR: It is shown that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
Journal ArticleDOI
A survey on deep learning in medical image analysis
Geert Litjens,Thijs Kooi,Babak Ehteshami Bejnordi,Arnaud Arindra Adiyoso Setio,Francesco Ciompi,Mohsen Ghafoorian,Jeroen van der Laak,Bram van Ginneken,Clara I. Sánchez +8 more
TL;DR: This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year, to survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks.
Journal ArticleDOI
Estimates of the prevalence of arthritis and other rheumatic conditions in the United States. Part II.
Reva C. Lawrence,David T. Felson,Charles G. Helmick,Lesley M. Arnold,Hyon K. Choi,Richard A. Deyo,Sherine E. Gabriel,Rosemarie Hirsch,Marc C. Hochberg,Gene G. Hunder,Joanne M. Jordan,Jeffrey N. Katz,Hilal Maradit Kremers,Frederick Wolfe +13 more
TL;DR: This report provides the best available prevalence estimates for the US for osteoarthritis, polymyalgia rheumatica, gout, fibromyalgia, and carpal tunnel syndrome as well as the symptoms of neck and back pain.