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A Single-Shot Regio...
A Single-Shot Region-Adaptive Network for Myotendinous Junction Segmentation in Muscular Ultrasound Images
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- Zhou, Guang-Quan (författare)
- Southeast Univ, Sch Biol Sci & Med Engn, Nanjing 210096, Peoples R China.
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- Huo, En-Ze (författare)
- Southeast Univ, Sch Biol Sci & Med Engn, Nanjing 210096, Peoples R China.
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- Yuan, Mei (författare)
- Jiangsu Prov Hosp, Dept Radiol, Nanjing 210029, Peoples R China.;Nanjing Med Univ, Affiliated Hosp 1, Nanjing 210029, Peoples R China.
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- Zhou, Ping (författare)
- Southeast Univ, Sch Biol Sci & Med Engn, Nanjing 210096, Peoples R China.
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- Wang, Ruoli (författare)
- KTH,Mekanik,Karolinska Inst, Dept Womens & Childrens Hlth, S-17177 Stockholm, Sweden.;Royal Inst Technol, KTH BioMEx Ctr, S-10044 Stockholm, Sweden.
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- Wang, Kai-Ni (författare)
- Southeast Univ, Sch Biol Sci & Med Engn, Nanjing 210096, Peoples R China.
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- Chen, Yang (författare)
- Southeast Univ, Sch Comp Sci & Engn, Nanjing 210046, Peoples R China.
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- He, Xiao-Pu (författare)
- Nanjing Med Univ, Affiliated Hosp 1, Nanjing 210029, Peoples R China.;Jiangsu Prov Hosp, Dept Geriatr, Nanjing 210029, Peoples R China.
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Southeast Univ, Sch Biol Sci & Med Engn, Nanjing 210096, Peoples R China Jiangsu Prov Hosp, Dept Radiol, Nanjing 210029, Peoples R China.;Nanjing Med Univ, Affiliated Hosp 1, Nanjing 210029, Peoples R China. (creator_code:org_t)
- Institute of Electrical and Electronics Engineers (IEEE), 2020
- 2020
- Engelska.
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Ingår i: IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control. - : Institute of Electrical and Electronics Engineers (IEEE). - 0885-3010 .- 1525-8955. ; 67:12, s. 2531-2542
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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http://kipublication...
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Abstract
Ämnesord
Stäng
- Tracking the myotendinous junction (MTJ) in consecutive ultrasound images is crucial for understanding the mechanics and pathological conditions of the muscletendon unit. However, the lack of reliable and efficient identification of MTJ due to poor image quality and boundary ambiguity restricts its application in motion analysis. In recent years, with the rapid development of deep learning, the region-based convolution neural network (RCNN) has shown great potential in the field of simultaneous objection detection and instance segmentation in medical images. This article proposes a region-adaptive network (RAN) to localize MTJ region and to segment it in a single shot. Our model learns about the salient information of MTJ with the help of a composite architecture. Herein, a region-based multitask learning network explores the region containing MTJ, while a parallel end-to-end U-shaped path extracts the MTJ structure from the adaptively selected region for combating data imbalance and boundary ambiguity. By demonstrating the ultrasound images of the gastrocnemius, we showed that the RAN achieves superior segmentation performance when compared with the state-of-the-art Mask RCNN method with an average Dice score of 80.1. Our proposed method is robust and reliable for advanced muscle and tendon function examinations obtained by ultrasound imaging.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering -- Medical Image Processing (hsv//eng)
Nyckelord
- Ultrasonic imaging
- Image segmentation
- Biomedical imaging
- Muscles
- Tendons
- Feature extraction
- Junctions
- Adaptive region
- deep learning
- instance segmentation
- myotendinous junction (MTJ)
- ultrasound
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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