131 research outputs found
Concurrent Segmentation and Localization for Tracking of Surgical Instruments
Real-time instrument tracking is a crucial requirement for various
computer-assisted interventions. In order to overcome problems such as specular
reflections and motion blur, we propose a novel method that takes advantage of
the interdependency between localization and segmentation of the surgical tool.
In particular, we reformulate the 2D instrument pose estimation as heatmap
regression and thereby enable a concurrent, robust and near real-time
regression of both tasks via deep learning. As demonstrated by our experimental
results, this modeling leads to a significantly improved performance than
directly regressing the tool position and allows our method to outperform the
state of the art on a Retinal Microsurgery benchmark and the MICCAI EndoVis
Challenge 2015.Comment: I. Laina and N. Rieke contributed equally to this work. Accepted to
MICCAI 201
Facial Landmark Point Localization using Coarse-to-Fine Deep Recurrent Neural Network
The accurate localization of facial landmarks is at the core of face analysis
tasks, such as face recognition and facial expression analysis, to name a few.
In this work we propose a novel localization approach based on a Deep Learning
architecture that utilizes dual cascaded CNN subnetworks of the same length,
where each subnetwork in a cascade refines the accuracy of its predecessor. The
first set of cascaded subnetworks estimates heatmaps that encode the landmarks'
locations, while the second set of cascaded subnetworks refines the
heatmaps-based localization using regression, and also receives as input the
output of the corresponding heatmap estimation subnetwork. The proposed scheme
is experimentally shown to compare favorably with contemporary state-of-the-art
schemes
An Attention-Guided Deep Regression Model for Landmark Detection in Cephalograms
Cephalometric tracing method is usually used in orthodontic diagnosis and
treatment planning. In this paper, we propose a deep learning based framework
to automatically detect anatomical landmarks in cephalometric X-ray images. We
train the deep encoder-decoder for landmark detection, and combine global
landmark configuration with local high-resolution feature responses. The
proposed frame-work is based on 2-stage u-net, regressing the multi-channel
heatmaps for land-mark detection. In this framework, we embed attention
mechanism with global stage heatmaps, guiding the local stage inferring, to
regress the local heatmap patches in a high resolution. Besides, the Expansive
Exploration strategy improves robustness while inferring, expanding the
searching scope without increasing model complexity. We have evaluated our
framework in the most widely-used public dataset of landmark detection in
cephalometric X-ray images. With less computation and manually tuning, our
framework achieves state-of-the-art results
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Deep Learning-based Prescription of Cardiac MRI Planes.
PurposeTo develop and evaluate a system to prescribe imaging planes for cardiac MRI based on deep learning (DL)-based localization of key anatomic landmarks.Materials and methodsAnnotated landmarks on 892 long-axis (LAX) and 493 short-axis (SAX) cine steady-state free precession series from cardiac MR images were retrospectively collected between February 2012 and June 2017. U-Net-based heatmap regression was used for localization of cardiac landmarks, which were used to compute cardiac MRI planes. Performance was evaluated by comparing localization distances and plane angle differences between DL predictions and ground truth. The plane angulations from DL were compared with those prescribed by the technologist at the original time of acquisition. Data were split into 80% for training and 20% for testing, and results confirmed with fivefold cross-validation.ResultsOn LAX images, DL localized the apex within mean 12.56 mm ± 19.11 (standard deviation) and the mitral valve (MV) within 7.68 mm ± 6.91. On SAX images, DL localized the aortic valve within 5.78 mm ± 5.68, MV within 5.90 mm ± 5.24, pulmonary valve within 6.55 mm ± 6.39, and tricuspid valve within 6.39 mm ± 5.89. On the basis of these localizations, average angle bias and mean error of DL-predicted imaging planes relative to ground truth annotations were as follows: SAX, -1.27° ± 6.81 and 4.93° ± 4.86; four chambers, 0.38° ± 6.45 and 5.16° ± 3.80; three chambers, 0.13° ± 12.70 and 9.02° ± 8.83; and two chamber, 0.25° ± 9.08 and 6.53° ± 6.28, respectively.ConclusionDL-based anatomic localization is a feasible strategy for planning cardiac MRI planes. This approach can produce imaging planes comparable to those defined by ground truth landmarks.© RSNA, 2019 Supplemental material is available for this article
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