NHBS-Net: A feature fusion attention network for ultrasound neonatal hip bone segmentation

Ruhan Liu, Mengyao Liu, Bin Sheng, Huating Li, Ping Li, Haitao Song, Ping Zhang, Lixin Jiang, Dinggang Shen

Research output: Journal article publicationJournal articleAcademic researchpeer-review

20 Citations (Scopus)


Ultrasound is a widely used technology for diagnosing developmental dysplasia of the hip (DDH) because it does not use radiation. Due to its low cost and convenience, 2-D ultrasound is still the most common examination in DDH diagnosis. In clinical usage, the complexity of both ultrasound image standardization and measurement leads to a high error rate for sonographers. The automatic segmentation results of key structures in the hip joint can be used to develop a standard plane detection method that helps sonographers decrease the error rate. However, current automatic segmentation methods still face challenges in robustness and accuracy. Thus, we propose a neonatal hip bone segmentation network (NHBS-Net) for the first time for the segmentation of seven key structures. We design three improvements, an enhanced dual attention module, a two-class feature fusion module, and a coordinate convolution output head, to help segment different structures. Compared with current state-of-the-art networks, NHBS-Net gains outstanding performance accuracy and generalizability, as shown in the experiments. Additionally, image standardization is a common need in ultrasonography. The ability of segmentation-based standard plane detection is tested on a 50-image standard dataset. The experiments show that our method can help healthcare workers decrease their error rate from 6%-10% to 2%. In addition, the segmentation performance in another ultrasound dataset (fetal heart) demonstrates the ability of our network.

Original languageEnglish
Pages (from-to)3446-3458
Number of pages13
JournalIEEE Transactions on Medical Imaging
Issue number12
Publication statusPublished - Dec 2021


  • medical image segmentation
  • Neonatal hip bone segmentation
  • self-attention mechanism

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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