208 research outputs found
Multi-stage Suture Detection for Robot Assisted Anastomosis based on Deep Learning
In robotic surgery, task automation and learning from demonstration combined
with human supervision is an emerging trend for many new surgical robot
platforms. One such task is automated anastomosis, which requires bimanual
needle handling and suture detection. Due to the complexity of the surgical
environment and varying patient anatomies, reliable suture detection is
difficult, which is further complicated by occlusion and thread topologies. In
this paper, we propose a multi-stage framework for suture thread detection
based on deep learning. Fully convolutional neural networks are used to obtain
the initial detection and the overlapping status of suture thread, which are
later fused with the original image to learn a gradient road map of the thread.
Based on the gradient road map, multiple segments of the thread are extracted
and linked to form the whole thread using a curvilinear structure detector.
Experiments on two different types of sutures demonstrate the accuracy of the
proposed framework.Comment: Submitted to ICRA 201
UNet-2022: Exploring Dynamics in Non-isomorphic Architecture
Recent medical image segmentation models are mostly hybrid, which integrate
self-attention and convolution layers into the non-isomorphic architecture.
However, one potential drawback of these approaches is that they failed to
provide an intuitive explanation of why this hybrid combination manner is
beneficial, making it difficult for subsequent work to make improvements on top
of them. To address this issue, we first analyze the differences between the
weight allocation mechanisms of the self-attention and convolution. Based on
this analysis, we propose to construct a parallel non-isomorphic block that
takes the advantages of self-attention and convolution with simple
parallelization. We name the resulting U-shape segmentation model as UNet-2022.
In experiments, UNet-2022 obviously outperforms its counterparts in a range
segmentation tasks, including abdominal multi-organ segmentation, automatic
cardiac diagnosis, neural structures segmentation, and skin lesion
segmentation, sometimes surpassing the best performing baseline by 4%.
Specifically, UNet-2022 surpasses nnUNet, the most recognized segmentation
model at present, by large margins. These phenomena indicate the potential of
UNet-2022 to become the model of choice for medical image segmentation.Comment: Code is available at https://bit.ly/3ggyD5
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
This two-volume set LNCS 12962 and 12963 constitutes the thoroughly refereed proceedings of the 7th International MICCAI Brainlesion Workshop, BrainLes 2021, as well as the RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge, the Federated Tumor Segmentation (FeTS) Challenge, the Cross-Modality Domain Adaptation (CrossMoDA) Challenge, and the challenge on Quantification of Uncertainties in Biomedical Image Quantification (QUBIQ). These were held jointly at the 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020, in September 2021. The 91 revised papers presented in these volumes were selected form 151 submissions. Due to COVID-19 pandemic the conference was held virtually. This is an open access book
A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery
Semantic segmentation (classification) of Earth Observation imagery is a
crucial task in remote sensing. This paper presents a comprehensive review of
technical factors to consider when designing neural networks for this purpose.
The review focuses on Convolutional Neural Networks (CNNs), Recurrent Neural
Networks (RNNs), Generative Adversarial Networks (GANs), and transformer
models, discussing prominent design patterns for these ANN families and their
implications for semantic segmentation. Common pre-processing techniques for
ensuring optimal data preparation are also covered. These include methods for
image normalization and chipping, as well as strategies for addressing data
imbalance in training samples, and techniques for overcoming limited data,
including augmentation techniques, transfer learning, and domain adaptation. By
encompassing both the technical aspects of neural network design and the
data-related considerations, this review provides researchers and practitioners
with a comprehensive and up-to-date understanding of the factors involved in
designing effective neural networks for semantic segmentation of Earth
Observation imagery.Comment: 145 pages with 32 figure
Neural Networks for Constitutive Modeling -- From Universal Function Approximators to Advanced Models and the Integration of Physics
Analyzing and modeling the constitutive behavior of materials is a core area
in materials sciences and a prerequisite for conducting numerical simulations
in which the material behavior plays a central role. Constitutive models have
been developed since the beginning of the 19th century and are still under
constant development. Besides physics-motivated and phenomenological models,
during the last decades, the field of constitutive modeling was enriched by the
development of machine learning-based constitutive models, especially by using
neural networks. The latter is the focus of the present review, which aims to
give an overview of neural networks-based constitutive models from a methodical
perspective. The review summarizes and compares numerous conceptually different
neural networks-based approaches for constitutive modeling including neural
networks used as universal function approximators, advanced neural network
models and neural network approaches with integrated physical knowledge. The
upcoming of these methods is in-turn closely related to advances in the area of
computer sciences, what further adds a chronological aspect to this review. We
conclude this review paper with important challenges in the field of learning
constitutive relations that need to be tackled in the near future
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