How Bad is Good enough: Noisy annotations for instrument pose estimation

06/20/2018
by   David Kügler, et al.
0

Though analysis of Medical Images by Deep Learning achieves unprecedented results across various applications, the effect of noisy training annotations is rarely studied in a systematic manner. In Medical Image Analysis, most reports addressing this question concentrate on studying segmentation performance of deep learning classifiers. The absence of continuous ground truth annotations in these studies limits the value of conclusions for applications, where regression is the primary method of choice. In the application of surgical instrument pose estimation, where precision has a direct clinical impact on patient outcome, studying the effect of noisy annotations on deep learning pose estimation techniques is of supreme importance. Real x-ray images are inadequate for this evaluation due to the unavailability of ground truth annotations. We circumvent this problem by generating synthetic radiographs, where the ground truth pose is known and therefore the pose estimation error made by the medical-expert can be estimated from experiments. Furthermore, this study shows the property of deep neural networks to learn dominant signals from noisy annotations with sufficient data in a regression setting.

READ FULL TEXT
research
02/24/2021

PFRL: Pose-Free Reinforcement Learning for 6D Pose Estimation

6D pose estimation from a single RGB image is a challenging and vital ta...
research
04/10/2016

Synthesizing Training Images for Boosting Human 3D Pose Estimation

Human 3D pose estimation from a single image is a challenging task with ...
research
06/26/2020

AutoSNAP: Automatically Learning Neural Architectures for Instrument Pose Estimation

Despite recent successes, the advances in Deep Learning have not yet bee...
research
12/25/2022

TexPose: Neural Texture Learning for Self-Supervised 6D Object Pose Estimation

In this paper, we introduce neural texture learning for 6D object pose e...
research
07/08/2021

A Dataset and Method for Hallux Valgus Angle Estimation Based on Deep Learing

Angular measurements is essential to make a resonable treatment for Hall...
research
07/31/2020

Disentangling Human Error from the Ground Truth in Segmentation of Medical Images

Recent years have seen increasing use of supervised learning methods for...
research
05/31/2017

Adversarial Inverse Graphics Networks: Learning 2D-to-3D Lifting and Image-to-Image Translation from Unpaired Supervision

Researchers have developed excellent feed-forward models that learn to m...

Please sign up or login with your details

Forgot password? Click here to reset