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KMID : 1137820200410050195
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의공학회지
2020 Volume.41 No. 5 p.195 ~ p.202
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Development and Evaluation of D-Attention Unet Model Using 3D and Continuous Visual Context for Needle Detection in Continuous Ultrasound Images
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Lee So-Hee
Kim Jong-Un
Lee Su-Yeol
Ryu Jeong-Won
Choi Dong-Hyuk
Tae Ki-Sik
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Abstract
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Needle detection in ultrasound images is sometimes difficult due to obstruction of fat tissues. Accurate needle detection using continuous ultrasound (CUS) images is a vital stage of treatment planning for tissue biopsy and brachytherapy. The main goal of the study is classified into two categories. First, new detection model, i.e. DAttention Unet, is developed by combining the context information of 3D medical data and CUS images. Second, the D-Attention Unet model was compared with other models to verify its usefulness for needle detection in continuous ultrasound images. The continuous needle images taken with ultrasonic waves were converted into still images for dataset to evaluate the performance of the D-Attention Unet. The dataset was used for training and testing. Based on the results, the proposed D-Attention Unet model showed the better performance than other 3 models (Unet, D-Unet and Attention Unet), with Dice Similarity Coefficient (DSC), Recall and Precision at 71.9%, 70.6% and 73.7%, respectively. In conclusion, the D-Attention Unet model provides accurate needle detection for US-guided biopsy or brachytherapy, facilitating the clinical workflow. Especially, this kind of research is enthusiastically being performed on how to add image processing techniques to learning techniques. Thus, the proposed method is applied in this manner, it will be more effective technique than before.
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KEYWORD
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Needle detection, Continuous ultrasound (CUS) images, Biopsy, Brachytherapy, Context information, D-Attention Unet
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FullTexts / Linksout information
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