|Statement||Roland Wilson and Michael Spann.|
|Series||Electronic & electrical engineering research studies., 9|
|Contributions||Spann, Michael, Ph. D.|
|LC Classifications||TA1632 .W55 1988|
|The Physical Object|
|Pagination||x, 180 p. :|
|Number of Pages||180|
|LC Control Number||87028510|
Additional Physical Format: Online version: Wilson, Roland, Image segmentation and uncertainty. Letchworth, Herts., England: Research Studies Press ; New York. Image Segmentation: Errors, sensitivity, and uncertainty Published in: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and . We use the PHiSeg network to segment the images, and additionally use the generated uncertainty information in a novel QC process to identify uncertain (and potentially inaccurate) segmentations. network (DNN) framework for image segmentation, enabling us to deﬁne a principled measure of uncertainty associated with label probabilities. Our framework estimates uncertainty analytically at test time, unlike the state of the art that relies on approximate and expensive algorithms.
Semantic segmentation of anatomical structures and pathologies is a crucial step in clinical diagnosis and many downstream tasks. The majority of recent automated segmentation methods treat the problem as a one-to-one mapping from image to output mask (e.g. ).However, medical segmentation problems are often characterised by ambiguities and multiple hypotheses may be plausible . There are much uncertainty in the process of image segmentation, The paper firstly researched the sources of uncertainty of image segmentation; and then analyzed the method of image segmentation based on K means cluster, and the method based on fuzzy K means cluster; and then, the paper researched the theory of cloud model, which considers the fuzziness, random and the their . Although many uncertainty estimation methods have been proposed for deep learning, little is known on their benefits and current challenges for medical image segmentation. Therefore, we report results of evaluating common voxel-wise uncertainty measures with respect to their reliability, and limitations on two medical image segmentation datasets. Tensorflow "Bayesian U-Net" aka BUNet. This is the source code for the MICCAI Paper, Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation (Nair et al.), of which I am the first author. The network architecture is a heavily modified U-Net (Ronneberger et al.), developed in network is augmented to provide the .
Results on 3D Brain Tumor Segmentation 2, Uncertainty Estimation G. Wang et al. , Test-time augmentation with uncertainty estimation for deep learning-based medical image segmentation FLAIR and ground Baseline TTD TTA TTA + TTD truth Flipping rotation scaling noise Multi-modal images - FLAIR - T1 - T1C - T2 Dataset - Train: - Valid: Uncertainty Guided Semi-supervised Segmentation of Retinal Layers in OCT Images In book: Medical Image Computing and Computer Assisted Intervention . The aim of image segmentation is to segment the different feature, so there is uncertainty in the process of image segmentaion. 3 METHODS OF IMAGE SEGMENTATION WITH UNCERTAINTY Usual methods of â hardâ image segmentation The Research about image segmentation is highly focused by people all the time, and the researchers have put forward. Medical Image Computing and Computer Assisted Intervention – MICCAI 22nd International Conference, Shenzhen, China, October 13–17, , Proceedings, Part II Capturing Uncertainty in Medical Image Segmentation. Christian F. Baumgartner, Kerem C. Tezcan, Krishna Chaitanya, Andreas M. Hötker, Urs J. Muehlematter, Khoschy Schawkat et.