Evaluating segmentation error without ground truth

Authors:
Timo Kohlberger;Vivek Singh;Chris Alvino;Claus Bahlmann;Leo Grady
Affiliations:
Corporate Research and Technology, Imaging and Computer Vision, Siemens Corp., Princeton, NJ;Corporate Research and Technology, Imaging and Computer Vision, Siemens Corp., Princeton, NJ;American Science and Engineering, Billerica, MA;Corporate Research and Technology, Imaging and Computer Vision, Siemens Corp., Princeton, NJ;HeartFlow, Inc., Redwood City, CA
Venue:
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Year:
2012

Citing 13
Cited 0

Visual reconstruction

Visual reconstruction
Geodesic Active Contours

International Journal of Computer Vision
Minimal Surfaces Based Object Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Interactive Organ Segmentation Using Graph Cuts

MICCAI '00 Proceedings of the Third International Conference on Medical Image Computing and Computer-Assisted Intervention
Probabilistic Boosting-Tree: Learning Discriminative Models for Classification, Recognition, and Clustering

ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Isoperimetric Graph Partitioning for Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Random Walks for Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
The WEKA data mining software: an update

ACM SIGKDD Explorations Newsletter
On simulating subjective evaluation using combined objective metrics for validation of 3D tumor segmentation

MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Automating image segmentation verification and validation by learning test oracles

Information and Software Technology
Automatic multi-organ segmentation using learning-based segmentation and level set optimization

MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
Improvements to the SMO algorithm for SVM regression

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

The automatic delineation of the boundaries of organs and other anatomical structures is a key component of many medical image processing systems. In this paper we present a generic learning approach based on a novel space of segmentation features, which can be trained to predict the overlap error and Dice coefficient of an arbitrary organ segmentation without knowing the ground truth delineation. We show the regressor to be much stronger a predictor of these error metrics than the responses of Probabilistic Boosting Classifiers trained on the segmentation boundary. The presented approach not only allows us to build reliable confidence measures and fidelity checks, but also to rank several segmentation hypotheses against each other during online usage of the segmentation algorithm in clinical practice.