Automatic Deep Learning Semantic Segmentation of Ultrasound Thyroid Cineclips Using Recurrent Fully Convolutional Networks

Medical segmentation is an important but challenging task with applications in standardized report generation, remote medicine and reducing medical exam costs by assisting experts. In this paper, we exploit time sequence information using a novel spatio-temporal recurrent deep learning network to au...

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Bibliographic Details
Published in:IEEE access 2021-01, Vol.9, p.5119-5127
Main Authors: Webb, Jeremy M., Meixner, Duane D., Adusei, Shaheeda A., Polley, Eric C., Fatemi, Mostafa, Alizad, Azra
Format: Article
Language:English
Subjects:
Adaptation models
Cysts
Deep learning
Nodules
Physical examinations
recurrent neural networks
Semantic segmentation
Semantics
Thyroid
Thyroid gland
thyroid nodule
thyroid volume
Training
Two dimensional displays
Ultrasonic imaging
Ultrasound
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Summary:Medical segmentation is an important but challenging task with applications in standardized report generation, remote medicine and reducing medical exam costs by assisting experts. In this paper, we exploit time sequence information using a novel spatio-temporal recurrent deep learning network to automatically segment the thyroid gland in ultrasound cineclips. We train a DeepLabv3+ based convolutional LSTM model in four stages to perform semantic segmentation by exploiting spatial context from ultrasound cineclips. The backbone DeepLabv3+ model is replicated six times and the output layers are replaced with convolutional LSTM layers in an atrous spatial pyramid pooling configuration. Our proposed model achieves mean intersection over union scores of 0.427 for cysts, 0.533 for nodules and 0.739 for thyroid. We demonstrate the potential application of convolutional LSTM models for thyroid ultrasound segmentation.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3045906