scholarly journals Dermoscopic Image Segmentation via Multistage Fully Convolutional Networks

2017 ◽  
Vol 64 (9) ◽  
pp. 2065-2074 ◽  
Author(s):  
Lei Bi ◽  
Jinman Kim ◽  
Euijoon Ahn ◽  
Ashnil Kumar ◽  
Michael Fulham ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Dermoscopic Image

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.

scholarly journals 3-D Fully Convolutional Networks for Multimodal Isointense Infant Brain Image Segmentation

2019 ◽  
Vol 49 (3) ◽  
pp. 1123-1136 ◽  
Author(s):  
Dong Nie ◽  
Li Wang ◽  
Ehsan Adeli ◽  
Cuijin Lao ◽  
Weili Lin ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Brain Image ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Infant Brain ◽  
Brain Image Segmentation

scholarly journals Stacked fully convolutional networks with multi-channel learning: application to medical image segmentation

2017 ◽  
Vol 33 (6-8) ◽  
pp. 1061-1071 ◽  
Author(s):  
Lei Bi ◽  
Jinman Kim ◽  
Ashnil Kumar ◽  
Michael Fulham ◽  
Dagan Feng
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Convolutional Networks ◽  
Fully Convolutional Networks
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