765 research outputs found
Breast pectoral muscle segmentation in mammograms using a modified holistically-nested edge detection network
This paper presents a method for automatic breast pectoral muscle segmentation in mediolateral oblique mammograms using a Convolutional Neural Network (CNN) inspired by the Holistically-nested Edge Detection (HED) network. Most of the existing methods in the literature are based on hand-crafted models such as straight-line, curve-based techniques or a combination of both. Unfortunately, such models are insufficient when dealing with complex shape variations of the pectoral muscle boundary and when the boundary is unclear due to overlapping breast tissue. To compensate for these issues, we propose a neural network framework that incorporates multi-scale and multi-level learning, capable of learning complex hierarchical features to resolve spatial ambiguity in estimating the pectoral muscle boundary. For this purpose, we modified the HED network architecture to specifically find ‘contour-like’ objects in mammograms. The proposed framework produced a probability map that can be used to estimate the initial pectoral muscle boundary. Subsequently, we process these maps by extracting morphological properties to find the actual pectoral muscle boundary. Finally, we developed two different post-processing steps to find the actual pectoral muscle boundary. Quantitative evaluation results show that the proposed method is comparable with alternative state-of-the-art methods producing on average values of 94.8 ± 8.5% and 97.5 ± 6.3% for the Jaccard and Dice similarity metrics, respectively, across four different databases
2017 Robotic Instrument Segmentation Challenge
In mainstream computer vision and machine learning, public datasets such as
ImageNet, COCO and KITTI have helped drive enormous improvements by enabling
researchers to understand the strengths and limitations of different algorithms
via performance comparison. However, this type of approach has had limited
translation to problems in robotic assisted surgery as this field has never
established the same level of common datasets and benchmarking methods. In 2015
a sub-challenge was introduced at the EndoVis workshop where a set of robotic
images were provided with automatically generated annotations from robot
forward kinematics. However, there were issues with this dataset due to the
limited background variation, lack of complex motion and inaccuracies in the
annotation. In this work we present the results of the 2017 challenge on
robotic instrument segmentation which involved 10 teams participating in
binary, parts and type based segmentation of articulated da Vinci robotic
instruments
ToolNet: Holistically-Nested Real-Time Segmentation of Robotic Surgical Tools
Real-time tool segmentation from endoscopic videos is an essential part of
many computer-assisted robotic surgical systems and of critical importance in
robotic surgical data science. We propose two novel deep learning architectures
for automatic segmentation of non-rigid surgical instruments. Both methods take
advantage of automated deep-learning-based multi-scale feature extraction while
trying to maintain an accurate segmentation quality at all resolutions. The two
proposed methods encode the multi-scale constraint inside the network
architecture. The first proposed architecture enforces it by cascaded
aggregation of predictions and the second proposed network does it by means of
a holistically-nested architecture where the loss at each scale is taken into
account for the optimization process. As the proposed methods are for real-time
semantic labeling, both present a reduced number of parameters. We propose the
use of parametric rectified linear units for semantic labeling in these small
architectures to increase the regularization ability of the design and maintain
the segmentation accuracy without overfitting the training sets. We compare the
proposed architectures against state-of-the-art fully convolutional networks.
We validate our methods using existing benchmark datasets, including ex vivo
cases with phantom tissue and different robotic surgical instruments present in
the scene. Our results show a statistically significant improved Dice
Similarity Coefficient over previous instrument segmentation methods. We
analyze our design choices and discuss the key drivers for improving accuracy.Comment: Paper accepted at IROS 201
Object Contour and Edge Detection with RefineContourNet
A ResNet-based multi-path refinement CNN is used for object contour
detection. For this task, we prioritise the effective utilization of the
high-level abstraction capability of a ResNet, which leads to state-of-the-art
results for edge detection. Keeping our focus in mind, we fuse the high, mid
and low-level features in that specific order, which differs from many other
approaches. It uses the tensor with the highest-levelled features as the
starting point to combine it layer-by-layer with features of a lower
abstraction level until it reaches the lowest level. We train this network on a
modified PASCAL VOC 2012 dataset for object contour detection and evaluate on a
refined PASCAL-val dataset reaching an excellent performance and an Optimal
Dataset Scale (ODS) of 0.752. Furthermore, by fine-training on the BSDS500
dataset we reach state-of-the-art results for edge-detection with an ODS of
0.824.Comment: Keywords: Object Contour Detection, Edge Detection, Multi-Path
Refinement CN
CASENet: Deep Category-Aware Semantic Edge Detection
Boundary and edge cues are highly beneficial in improving a wide variety of
vision tasks such as semantic segmentation, object recognition, stereo, and
object proposal generation. Recently, the problem of edge detection has been
revisited and significant progress has been made with deep learning. While
classical edge detection is a challenging binary problem in itself, the
category-aware semantic edge detection by nature is an even more challenging
multi-label problem. We model the problem such that each edge pixel can be
associated with more than one class as they appear in contours or junctions
belonging to two or more semantic classes. To this end, we propose a novel
end-to-end deep semantic edge learning architecture based on ResNet and a new
skip-layer architecture where category-wise edge activations at the top
convolution layer share and are fused with the same set of bottom layer
features. We then propose a multi-label loss function to supervise the fused
activations. We show that our proposed architecture benefits this problem with
better performance, and we outperform the current state-of-the-art semantic
edge detection methods by a large margin on standard data sets such as SBD and
Cityscapes.Comment: Accepted to CVPR 201
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