Effect of different uncertainties in medical image segment error estimation

Effect of different uncertainties in medical image segment error estimation: Interactive segmentation of 3D medical images

Kim, Sungjin (TU Delft Electrical Engineering, Mathematics and Computer Science)

Hildebrandt, K.A. (mentor)
Chaves de Plaza, N.F. (mentor)
Abeel, T.E.P.M.F. (graduation committee)

Delft University of Technology

Computer Science and Engineering

CSE3000 Research Project

2023-06-28

Although automated segmentation of 3D medical images produce near-ideal results, they encounter limitations and occasional errors, necessitating manual intervention for error correction. Recent studies introduce an active learning pipeline as an efficient solution for this, requiring user corrections only on some of the most uncertain parts of
the automatically segmented image. It does so by combining different uncertainty fields, which are various ways to quantify possible errors. We investigate into its individual uncertainty fields and their combination scheme in attempt to validate its methods. Additionally, we replace its methods for estimating uncertainty with another common way to do so, called the ensemble method, to test possible improvements at uncertainty estimation. Results of this research validates the combination method of the active learning pipeline, and shows weak advantages but strong disadvantages of the ensemble method when compared to the combined method of the active learning pipeline.

Medical image segmentation
Uncertainty Estimation
Ensemble Method

http://resolver.tudelft.nl/uuid:dd42bb7e-3312-4332-ba67-dad8610c2fe0

Student theses

bachelor thesis

© 2023 Sungjin Kim