# Simultaneous Segmentation and Classification of Bone Surfaces from Ultrasound Using a Multi-feature Guided CNN

arXiv: https://arxiv.org/abs/1806.09766 · HTML: https://mlsanity.com/paper/1806.09766/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jun 2018.

## Built on by (most cited first, 16 of 16)

- [Modality specific U-Net variants for biomedical image segmentation: A survey](https://mlsanity.com/paper/2107.04537/) (2021)
- [Rethinking Ultrasound Augmentation: A Physics-Inspired Approach](https://mlsanity.com/paper/2105.02188/) (2021)
- [FetalNet: Multi-task Deep Learning Framework for Fetal Ultrasound Biometric Measurements](https://mlsanity.com/paper/2107.06943/) (2021)
- [Deblurring Masked Autoencoder is Better Recipe for Ultrasound Image Recognition](https://mlsanity.com/paper/2306.08249/) (2023)
- [Histogram of Oriented Gradients Meet Deep Learning: A Novel Multi-task Deep Network for Medical Image Semantic Segmentation](https://mlsanity.com/paper/2204.01712/) (2022)
- [Deep Learning Fetal Ultrasound Video Model Match Human Observers in Biometric Measurements](https://mlsanity.com/paper/2205.13835/) (2022)
- [Orientation-guided Graph Convolutional Network for Bone Surface Segmentation](https://mlsanity.com/paper/2206.08481/) (2022)
- [Semantic Segmentation Refiner for Ultrasound Applications with Zero-Shot Foundation Models](https://mlsanity.com/paper/2404.16325/) (2024)
- [Segmentation-Consistent Probabilistic Lesion Counting](https://mlsanity.com/paper/2204.05276/) (2022)
- [Simultaneous Bone and Shadow Segmentation Network using Task Correspondence Consistency](https://mlsanity.com/paper/2206.08936/) (2022)
- [Automatic segmentation of vertebral features on ultrasound spine images using Stacked Hourglass Network](https://mlsanity.com/paper/2105.03847/) (2021)
- [Learning the Imaging Landmarks: Unsupervised Key point Detection in Lung Ultrasound Videos](https://mlsanity.com/paper/2106.06987/) (2021)
- [Multi-Task Multi-Scale Learning For Outcome Prediction in 3D PET Images](https://mlsanity.com/paper/2203.00641/) (2022)
- [Feature-aggregated spatiotemporal spine surface estimation for wearable patch ultrasound volumetric imaging](https://mlsanity.com/paper/2211.05962/) (2022)
- [Analyzing Image Encoder Choices and Graph Homophily in GCN Frameworks for Breast Ultrasound Classification](https://mlsanity.com/paper/2607.12054/) (2026)
- [Learning from Complementary Ultrasound Representations for Liver Disease Classification](https://mlsanity.com/paper/2607.12062/) (2026)

## Abstract (arXiv)

Various imaging artifacts, low signal-to-noise ratio, and bone surfaces appearing several millimeters in thickness have hindered the success of ultrasound (US) guided computer assisted orthopedic surgery procedures. In this work, a multi-feature guided convolutional neural network (CNN) architecture is proposed for simultaneous enhancement, segmentation, and classification of bone surfaces from US data. The proposed CNN consists of two main parts: a pre-enhancing net, that takes the concatenation of B-mode US scan and three filtered image features for the enhancement of bone surfaces, and a modified U-net with a classification layer. The proposed method was validated on 650 in vivo US scans collected using two US machines, by scanning knee, femur, distal radius and tibia bones. Validation, against expert annotation, achieved statistically significant improvements in segmentation of bone surfaces compared to state-of-the-art.