PMED-Net: Pyramid Based Multi-Scale Encoder-Decoder Network for Medical Image Segmentation

A pyramidical multi-scale encoder-decoder network, namely PMED-Net, is proposed for medical image segmentation. Different variants of encoder-decoder networks are in practice for segmenting the medical images and U-Net is the most widely used one. However, the existing architectures for segmenting m...

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Bibliographic Details
Published in:IEEE access Vol. 9; pp. 55988 - 55998
Main Authors: Khan, Abbas, Kim, Hyongsuk, Chua, Leon
Format: Journal Article
Language:English
Published: Piscataway IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Coders
Convolutional neural networks
Datasets
Decoding
Deep learning
Diseases
encoder-decoder architecture
Encoders-Decoders
Feature extraction
Image segmentation
Medical diagnostic imaging
medical image processing
Medical imaging
Networks
Parameters
semantic segmentation
Training
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary:A pyramidical multi-scale encoder-decoder network, namely PMED-Net, is proposed for medical image segmentation. Different variants of encoder-decoder networks are in practice for segmenting the medical images and U-Net is the most widely used one. However, the existing architectures for segmenting medical images have millions of parameters that require enormous computations which results in memory and cost-inefficiency. To overcome such limitations, we come up with the idea of training small networks in a cascaded form for coarse-to-fine prediction. The proposed adaptive network is extended up to six pyramid levels, and at each level, features are extracted at different scales of the input image. Each lightweight encoder-decoder network is trained independently to minimize loss, where succeeding level networks further refine the prior predictions. Evaluation and comparison of our architecture were performed on four different publicly available medical image segmentation datasets: International Skin Imaging Collaboration (ISIC) challenge 2018 dataset, brain tumor dataset, nuclei dataset, and X-ray dataset. The experimental results of the PMED-Net are either better or on par with other state-of-the-art networks in terms of IoU, F1-Score, and sensitivity metrics. Moreover, PMED-Net is efficient in terms of parameterized complexity as it has 1/21.3, 1/21.1, 1/14.0, 1/11.6, 1/11.2, 1/6.64, and 1/4.95 times fewer parameters than SegNet, U-Net, BCDU-Net, CU-Net, FCN-8s, ORED-Net, and MultiResUNet respectively. The pre-trained models, datasets information, and implementation details are available at https://github.com/kabbas570/Pyramid-Based-Encoder-Decoder .
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3071754