Salt Segmentation with Fully Convolutional Networks and Transfer Learning

2018 ◽  
Author(s):  
P. M. Cruz ◽  
J. P. Navarro
Keyword(s):  
Transfer Learning ◽  
Convolutional Networks ◽  
Fully Convolutional Networks

scholarly journals Fully Convolutional Networks with Multiscale 3D Filters and Transfer Learning for Change Detection in High Spatial Resolution Satellite Images

2020 ◽  
Vol 12 (5) ◽  
pp. 799 ◽  
Author(s):  
Ahram Song ◽  
Jaewan Choi
Keyword(s):  
Change Detection ◽  
Transfer Learning ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Semantic Segmentation ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Optimal Size ◽  
Convolutional Networks ◽  
Fully Convolutional Networks

Remote sensing images having high spatial resolution are acquired, and large amounts of data are extracted from their region of interest. For processing these images, objects of various sizes, from very small neighborhoods to large regions composed of thousands of pixels, should be considered. To this end, this study proposes change detection method using transfer learning and recurrent fully convolutional networks with multiscale three-dimensional (3D) filters. The initial convolutional layer of the change detection network with multiscale 3D filters was designed to extract spatial and spectral features of materials having different sizes; the layer exploits pre-trained weights and biases of semantic segmentation network trained on an open benchmark dataset. The 3D filter sizes were defined in a specialized way to extract spatial and spectral information, and the optimal size of the filter was determined using highly accurate semantic segmentation results. To demonstrate the effectiveness of the proposed method, binary change detection was performed on images obtained from multi-temporal Korea multipurpose satellite-3A. Results revealed that the proposed method outperformed the traditional deep learning-based change detection methods and the change detection accuracy improved using multiscale 3D filters and transfer learning.

scholarly journals Transfer learning framework for cell segmentation with incorporation of geometric features

2021 ◽  
Author(s):  
Yinuo Jin ◽  
Alexandre Toberoff ◽  
Elham Azizi
Keyword(s):  
Transfer Learning ◽  
Training Data ◽  
Cell Segmentation ◽  
Great Success ◽  
Geometric Features ◽  
Convolutional Networks ◽  
Nuclei Segmentation ◽  
Fully Convolutional Networks

With recent advances in multiplexed imaging and spatial transcriptomic and proteomic technologies, cell segmentation is becoming a crucial step in biomedical image analysis. In recent years, Fully Convolutional Networks (FCN) have achieved great success in nuclei segmentation in in vitro imaging. Nevertheless, it remains challenging to perform similar tasks on in situ tissue images with more cluttered cells of diverse shapes. To address this issue, we propose a novel transfer learning, cell segmentation framework incorporating shape-aware features in a deep learning model, with multi-level watershed and morphological post-processing steps. Our results show that incorporation of geometric features improves generalizability to segmenting cells in in situ tissue images, using solely in vitro images as training data.

scholarly journals THE LAND COVER CLASSIFICATION USING A FEATURE PYRAMID NETWORKS ARCHITECTURE FROM SATELLITE IMAGERY

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 241-246
Author(s):  
Q. Zhang ◽  
Y. Zhang ◽  
P. Yang ◽  
Y. Meng ◽  
S. Zhuo ◽  
...  
Keyword(s):  
Land Cover ◽  
Transfer Learning ◽  
Satellite Imagery ◽  
Loss Function ◽  
Land Cover Classification ◽  
Deep Convolutional Neural Networks ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Sensing Applications ◽  
Feature Pyramid

Abstract. Extracting land cover information from satellite imagery is of great importance for the task of automated monitoring in various remote sensing applications. Deep convolutional neural networks make this task more feasible, but they are limited by the small dataset of annotated images. In this paper, we present a fully convolutional networks architecture, FPN-VGG, that combines Feature Pyramid Networks and VGG. In order to accomplish the task of land cover classification, we create a land cover dataset of pixel-wise annotated images, and employ a transfer learning step and the variant dice loss function to promote the performance of FPN-VGG. The results indicate that FPN-VGG shows more competence for land cover classification comparing with other state-of-the-art fully convolutional networks. The transfer learning and dice loss function are beneficial to improve the performance of on the small and unbalanced dataset. Our best model on the dataset gets an overall accuracy of 82.9%, an average F1 score of 66.0% and an average IoU of 52.7%.

scholarly journals Automatic Deep Learning Semantic Segmentation of Ultrasound Thyroid Cineclips using Recurrent Fully Convolutional Networks

IEEE Access ◽  
2020 ◽  
pp. 1-1
Author(s):  
Jeremy M. Webb ◽  
Duane D. Meixner ◽  
Shaheeda A. Adusei ◽  
Eric C. Polley ◽  
Mostafa Fatemi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Semantic Segmentation ◽  
Convolutional Networks ◽  
Fully Convolutional Networks

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.

Sign in / Sign up

Export Citation Format

Share Document