scholarly journals Deep Neural Network-Based Semantic Segmentation of Microvascular Decompression Images

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1167
Author(s):  
Ruifeng Bai ◽  
Shan Jiang ◽  
Haijiang Sun ◽  
Yifan Yang ◽  
Guiju Li
Keyword(s):  
Semantic Segmentation ◽  
Fine Tuning ◽  
Cerebral Vessel ◽  
Current State ◽  
Proposed Model ◽  
Segmentation Accuracy ◽  
Spatial Pyramid Pooling ◽  
Boundary Information ◽  
True Color ◽  
Good Research

Image semantic segmentation has been applied more and more widely in the fields of satellite remote sensing, medical treatment, intelligent transportation, and virtual reality. However, in the medical field, the study of cerebral vessel and cranial nerve segmentation based on true-color medical images is in urgent need and has good research and development prospects. We have extended the current state-of-the-art semantic-segmentation network DeepLabv3+ and used it as the basic framework. First, the feature distillation block (FDB) was introduced into the encoder structure to refine the extracted features. In addition, the atrous spatial pyramid pooling (ASPP) module was added to the decoder structure to enhance the retention of feature and boundary information. The proposed model was trained by fine tuning and optimizing the relevant parameters. Experimental results show that the encoder structure has better performance in feature refinement processing, improving target boundary segmentation precision, and retaining more feature information. Our method has a segmentation accuracy of 75.73%, which is 3% better than DeepLabv3+.

scholarly journals Semantic Segmentation of Underwater Images Based on Improved Deeplab

2020 ◽  
Vol 8 (3) ◽  
pp. 188
Author(s):  
Fangfang Liu ◽  
Ming Fang
Keyword(s):  
Semantic Segmentation ◽  
Autonomous Driving ◽  
Correction Method ◽  
Target Object ◽  
Original Method ◽  
Indoor Navigation ◽  
Fine Tuning ◽  
Object Boundary ◽  
Current State ◽  
Segmentation Accuracy

Image semantic segmentation technology has been increasingly applied in many fields, for example, autonomous driving, indoor navigation, virtual reality and augmented reality. However, underwater scenes, where there is a huge amount of marine biological resources and irreplaceable biological gene banks that need to be researched and exploited, are limited. In this paper, image semantic segmentation technology is exploited to study underwater scenes. We extend the current state-of-the-art semantic segmentation network DeepLabv3 + and employ it as the basic framework. First, the unsupervised color correction method (UCM) module is introduced to the encoder structure of the framework to improve the quality of the image. Moreover, two up-sampling layers are added to the decoder structure to retain more target features and object boundary information. The model is trained by fine-tuning and optimizing relevant parameters. Experimental results indicate that the image obtained by our method demonstrates better performance in improving the appearance of the segmented target object and avoiding its pixels from mingling with other class’s pixels, enhancing the segmentation accuracy of the target boundaries and retaining more feature information. Compared with the original method, our method improves the segmentation accuracy by 3%.

scholarly journals Semantic segmentation of gonio-photographs via adaptive ROI localisation and uncertainty estimation

2021 ◽  
Vol 6 (1) ◽  
pp. e000898
Author(s):  
Andrea Peroni ◽  
Anna Paviotti ◽  
Mauro Campigotto ◽  
Luis Abegão Pinto ◽  
Carlo Alberto Cutolo ◽  
...  
Keyword(s):  
Region Of Interest ◽  
Ground Truth ◽  
Semantic Segmentation ◽  
Uncertainty Estimation ◽  
Depth Of Field ◽  
Clinical Settings ◽  
Proposed Model ◽  
Validation Experiment ◽  
Segmentation Accuracy ◽  
Ground Truth Image

ObjectiveTo develop and test a deep learning (DL) model for semantic segmentation of anatomical layers of the anterior chamber angle (ACA) in digital gonio-photographs.Methods and analysisWe used a pilot dataset of 274 ACA sector images, annotated by expert ophthalmologists to delineate five anatomical layers: iris root, ciliary body band, scleral spur, trabecular meshwork and cornea. Narrow depth-of-field and peripheral vignetting prevented clinicians from annotating part of each image with sufficient confidence, introducing a degree of subjectivity and features correlation in the ground truth. To overcome these limitations, we present a DL model, designed and trained to perform two tasks simultaneously: (1) maximise the segmentation accuracy within the annotated region of each frame and (2) identify a region of interest (ROI) based on local image informativeness. Moreover, our calibrated model provides results interpretability returning pixel-wise classification uncertainty through Monte Carlo dropout.ResultsThe model was trained and validated in a 5-fold cross-validation experiment on ~90% of available data, achieving ~91% average segmentation accuracy within the annotated part of each ground truth image of the hold-out test set. An appropriate ROI was successfully identified in all test frames. The uncertainty estimation module located correctly inaccuracies and errors of segmentation outputs.ConclusionThe proposed model improves the only previously published work on gonio-photographs segmentation and may be a valid support for the automatic processing of these images to evaluate local tissue morphology. Uncertainty estimation is expected to facilitate acceptance of this system in clinical settings.

scholarly journals Multiscale Road Extraction in Remote Sensing Images

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Aziguli Wulamu ◽  
Zuxian Shi ◽  
Dezheng Zhang ◽  
Zheyu He
Keyword(s):  
Remote Sensing ◽  
Network Architecture ◽  
Semantic Segmentation ◽  
Road Extraction ◽  
Remote Sensing Images ◽  
The Road ◽  
Proposed Model ◽  
Different Types ◽  
Spatial Pyramid Pooling ◽  
The One

Recent advances in convolutional neural networks (CNNs) have shown impressive results in semantic segmentation. Among the successful CNN-based methods, U-Net has achieved exciting performance. In this paper, we proposed a novel network architecture based on U-Net and atrous spatial pyramid pooling (ASPP) to deal with the road extraction task in the remote sensing field. On the one hand, U-Net structure can effectively extract valuable features; on the other hand, ASPP is able to utilize multiscale context information in remote sensing images. Compared to the baseline, this proposed model has improved the pixelwise mean Intersection over Union (mIoU) of 3 points. Experimental results show that the proposed network architecture can deal with different types of road surface extraction tasks under various terrains in Yinchuan city, solve the road connectivity problem to some extent, and has certain tolerance to shadows and occlusion.

scholarly journals Bridge Crack Semantic Segmentation Based on Improved Deeplabv3+

2021 ◽  
Vol 9 (6) ◽  
pp. 671
Author(s):  
Huixuan Fu ◽  
Dan Meng ◽  
Wenhui Li ◽  
Yuchao Wang
Keyword(s):  
Crack Detection ◽  
Detection Method ◽  
Semantic Segmentation ◽  
Learning Technology ◽  
Data Set ◽  
Original Algorithm ◽  
Bridge Maintenance ◽  
Segmentation Accuracy ◽  
Spatial Pyramid Pooling ◽  
Traditional Image

Cracks are the main goal of bridge maintenance and accurate detection of cracks will help ensure their safe use. Aiming at the problem that traditional image processing methods are difficult to accurately detect cracks, deep learning technology was introduced and a crack detection method based on an improved DeepLabv3+ semantic segmentation algorithm was proposed. In the network structure, the densely connected atrous spatial pyramid pooling module was introduced into the DeepLabv3+ network, which enabled the network to obtain denser pixel sampling, thus enhancing the ability of the network to extract detail features. While obtaining a larger receptive field, the number of network parameters was consistent with the original algorithm. The images of bridge cracks under different environmental conditions were collected, and then a concrete bridge crack segmentation data set was established, and the segmentation model was obtained through end-to-end training of the network. The experimental results showed that the improved DeepLabv3+ algorithm had higher crack segmentation accuracy than the original DeepLabv3+ algorithm, with an average intersection ratio reaching 82.37%, and the segmentation of crack details was more accurate, which proved the effectiveness of the proposed algorithm.

scholarly journals T-MIS: Transparency Adaptation in Medical Image Segmentation

2021 ◽  
Vol 1 (1) ◽  
pp. 11-13
Author(s):  
Ayush Somani ◽  
Divij Singh ◽  
Dilip Prasad ◽  
Alexander Horsch
Keyword(s):  
Image Features ◽  
Medical Image Segmentation ◽  
Fine Tuning ◽  
User Interactions ◽  
Training Samples ◽  
Proposed Model ◽  
Segmentation Accuracy ◽  
Segmentation Task ◽  
Unseen Objects ◽  
Medical Segmentation

We often locate ourselves in a trade-off situation between what is predicted and understanding why the predictive modeling made such a prediction. This high-risk medical segmentation task is no different where we try to interpret how well has the model learned from the image features irrespective of its accuracy. We propose image-specific fine-tuning to make a deep learning model adaptive to specific medical imaging tasks. Experimental results reveal that: a) proposed model is more robust to segment previously unseen objects (negative test dataset) than state-of-the-art CNNs; b) image-specific fine-tuning with the proposed heuristics significantly enhances segmentation accuracy; and c) our model leads to accurate results with fewer user interactions and less user time than conventional interactive segmentation methods. The model successfully classified ’no polyp’ or ’no instruments’ in the image irrespective of the absence of negative data in training samples from Kvasir-seg and Kvasir-Instrument datasets.

scholarly journals Road Network Extraction Using Atrous Spatial Pyramid Pooling

2019 ◽  
Vol 8 (9) ◽  
pp. 31-33
Keyword(s):  
Road Network ◽  
Spatial Information ◽  
Semantic Segmentation ◽  
Low Level ◽  
Multi Scale ◽  
Road Network Extraction ◽  
Proposed Model ◽  
Spatial Pyramid Pooling ◽  
Segmentation Image ◽  
Spatial Pyramid

Road extraction from satellite images has several Applications such as geographic information system (GIS). Having an accurate and up-to-date road network database will facilitate transportation, disaster management and GPS navigation. Most active field of research for automatic extraction of road network involves semantic segmentation using convolutional neural network (CNN). Although they can produce accurate results, typically the models give up performance for accuracy and vice-versa. In this paper, we are proposing architecture for semantic segmentation of road networks using Atrous Spatial Pyramid Pooling (ASPP). The network contains residual blocks for extracting low level features. Atrous convolutions with different dilation rates are taken and spatial pyramid pooling is performed on these features for extracting the spatial information. The low level features from residual blocks are added to the multi scale context information to produce the final segmentation image. Our proposed model significantly reduces the number of parameters that are required to train the model. The proposed model was trained on the Massachusetts roads dataset and the results have shown that our model produces superior results than that of popular state-of-the art models.

scholarly journals Semantic Segmentation by Multi-Scale Feature Extraction Based on Grouped Dilated Convolution Module

Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 947
Author(s):  
Dong Seop Kim ◽  
Yu Hwan Kim ◽  
Kang Ryoung Park
Keyword(s):  
State Of The Art ◽  
Receptive Fields ◽  
Semantic Segmentation ◽  
Video Database ◽  
Excellent Performance ◽  
Multi Scale ◽  
Dilated Convolution ◽  
Proposed Model ◽  
Depth Analysis ◽  
Spatial Pyramid Pooling

Existing studies have shown that effective extraction of multi-scale information is a crucial factor directly related to the increase in performance of semantic segmentation. Accordingly, various methods for extracting multi-scale information have been developed. However, these methods face problems in that they require additional calculations and vast computing resources. To address these problems, this study proposes a grouped dilated convolution module that combines existing grouped convolutions and atrous spatial pyramid pooling techniques. The proposed method can learn multi-scale features more simply and effectively than existing methods. Because each convolution group has different dilations in the proposed model, they have receptive fields of different sizes and can learn features corresponding to these receptive fields. As a result, multi-scale context can be easily extracted. Moreover, optimal hyper-parameters are obtained from an in-depth analysis, and excellent segmentation performance is derived. To evaluate the proposed method, open databases of the Cambridge Driving Labeled Video Database (CamVid) and the Stanford Background Dataset (SBD) are utilized. The experimental results indicate that the proposed method shows a mean intersection over union of 73.15% based on the CamVid dataset and 72.81% based on the SBD, thereby exhibiting excellent performance compared to other state-of-the-art methods.

scholarly journals Automatic Detection of Pediatric Craniofacial Deformities using Convolutional Neural Networks

2020 ◽  
Vol 6 (3) ◽  
pp. 338-340
Author(s):  
Wattendorf Sonja ◽  
Tabatabaei Seyed Amir Hossein ◽  
Fischer Patrick ◽  
Hans-Peter Hans-Peter ◽  
Martina Wilbrand ◽  
...  
Keyword(s):  
Neural Network ◽  
Mobile Application ◽  
Therapy Monitoring ◽  
Semantic Segmentation ◽  
Learning Technologies ◽  
Skull Shape ◽  
Proposed Model ◽  
Segmentation Accuracy ◽  
Abnormal Head ◽  
Modern Machine

AbstractThe geometric shape of our skull is very important, not only from an esthetic perspective, but also from medical viewpoint. However, the lack of designated medical experts and wrong positioning is leading to an increasing number of abnormal head shapes in newborns and infants. To make screening and therapy monitoring for these abnormal shapes easier, we develop a mobile application to automatically detect and quantify such shapes. By making use of modern machine learning technologies like deep learning and transfer learning, we have developed a convolutional neural network for semantic segmentation of bird’s-eye view images of child heads. Using this approach, we have been able to achieve a segmentation accuracy of approximately 99 %, while having sensitivity and specificity of above 98 %. Given these promising results, we will use this basis to calculate medical parameters to quantify the skull shape. In addition, we will integrate the proposed model into a mobile application for further validation and usage in a real-world scenario.

FINE-TUNING OF MODELLING STRATEGY TO SIMULATE THERMO-MECHANICAL BEHAVIOUR OF DOUBLE FRICTION PENDULUM SEISMIC ISOLATORS UST ESTIMATOR

2019 ◽  
Vol 3 (Special Issue on First SACEE'19) ◽  
pp. 165-172
Author(s):  
Vincenzo Bianco ◽  
Giorgio Monti ◽  
Nicola Pio Belfiore
Keyword(s):  
Multibody Dynamics ◽  
Mechanical Behaviour ◽  
Mechanical Model ◽  
Dynamic Behaviour ◽  
Experimental Testing ◽  
Fine Tuning ◽  
Proposed Model ◽  
Modelling Techniques ◽  
Friction Pendulum

The use of friction pendulum devices has recently attracted the attention of both academic and professional engineers for the protection of structures in seismic areas. Although the effectiveness of these has been shown by the experimental testing carried out worldwide, many aspects still need to be investigated for further improvement and optimisation. A thermo-mechanical model of a double friction pendulum device (based on the most recent modelling techniques adopted in multibody dynamics) is presented in this paper. The proposed model is based on the observation that sliding may not take place as ideally as is indicated in the literature. On the contrary, the fulfilment of geometrical compatibility between the constitutive bodies (during an earthquake) suggests a very peculiar dynamic behaviour composed of a continuous alternation of sticking and slipping phases. The thermo-mechanical model of a double friction pendulum device (based on the most recent modelling techniques adopted in multibody dynamics) is presented. The process of fine-tuning of the selected modelling strategy (available to date) is also described.

scholarly journals Boundary Loss-Based 2.5D Fully Convolutional Neural Networks Approach for Segmentation: A Case Study of the Liver and Tumor on Computed Tomography

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 144
Author(s):  
Yuexing Han ◽  
Xiaolong Li ◽  
Bing Wang ◽  
Lu Wang
Keyword(s):  
Image Segmentation ◽  
Spatial Information ◽  
Medical Images ◽  
Tumor Segmentation ◽  
Convolutional Networks ◽  
Learning Framework ◽  
Fully Convolutional Networks ◽  
Segmentation Accuracy ◽  
Boundary Information

Image segmentation plays an important role in the field of image processing, helping to understand images and recognize objects. However, most existing methods are often unable to effectively explore the spatial information in 3D image segmentation, and they neglect the information from the contours and boundaries of the observed objects. In addition, shape boundaries can help to locate the positions of the observed objects, but most of the existing loss functions neglect the information from the boundaries. To overcome these shortcomings, this paper presents a new cascaded 2.5D fully convolutional networks (FCNs) learning framework to segment 3D medical images. A new boundary loss that incorporates distance, area, and boundary information is also proposed for the cascaded FCNs to learning more boundary and contour features from the 3D medical images. Moreover, an effective post-processing method is developed to further improve the segmentation accuracy. We verified the proposed method on LITS and 3DIRCADb datasets that include the liver and tumors. The experimental results show that the performance of the proposed method is better than existing methods with a Dice Per Case score of 74.5% for tumor segmentation, indicating the effectiveness of the proposed method.

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