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

Source: [Simultaneous Segmentation and Classification of Bone Surfaces from Ultrasound Using a Multi-feature Guided CNN](<https://www.sota2.com/research/paper/68fa64851e209131cea051aa>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:06:03.000Z

Authors: Puyang Wang, Vishal M. Patel, Ilker Hacihaliloglu

Published: 2018-06-26T02:35:50.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Bone Surface Segmentation on SonixTouch (5-fold cross-validation test)](<https://www.sota2.com/research/sota/bone-surface-segmentation-on-sonixtouch-5-fold-cross-validation-test.md>): Bone surface segmentation; SonixTouch 5-fold cross-validation (test); Dice Score: 0.8105.

- [Bone Surface Segmentation on Clarius (5-fold cross-validation test)](<https://www.sota2.com/research/sota/bone-surface-segmentation-on-clarius-5-fold-cross-validation-test.md>): Bone surface segmentation; Clarius 5-fold cross-validation (test); Dice Score: 82.23.

- [Bone Surface Segmentation on SonixTouch](<https://www.sota2.com/research/sota/bone-surface-segmentation-on-sonixtouch.md>): Bone surface segmentation; SonixTouch; APLS: 67.65.

- [Thyroid nodule classification on Hospital Ultrasound dataset (test)](<https://www.sota2.com/research/sota/thyroid-nodule-classification-on-hospital-ultrasound-dataset-test.md>): Thyroid nodule classification; Hospital Ultrasound dataset (test); Accuracy: 87.44.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1806.09766>)

[PDF](<https://arxiv.org/pdf/1806.09766.pdf>)