Titlebook: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015; 18th International C Nassir Navab,Joachim Hornegger,Alejandro F.

流行

Statistical Power in Image Segmentation: Relating Sample Size to Reference Standard Quality segmentation algorithms? (2) How accurate should the reference standard be? The resulting formula predicted statistical power to within 2% of Monte Carlo simulations across a range of model parameters. A case study, using the PROMISE12 prostate segmentation data set, shows the practical use of the formula.

Sedative

一回合

A Latent Source Model for Patch-Based Image Segmentationnd theory for nonparametric classification. We use the model to derive a new patch-based segmentation algorithm that iterates between inferring local label patches and merging these local segmentations to produce a globally consistent image segmentation. Many existing patch-based algorithms arise as special cases of the new algorithm.

Expurgate

Deep Convolutional Encoder Networks for Multiple Sclerosis Lesion Segmentationons in magnetic resonance images. Our model is a neural network that has both convolutional and deconvolutional layers, and combines feature extraction and segmentation prediction in a single model. The joint training of the feature extraction and prediction layers allows the model to automatically

Glucocorticoids

Unsupervised Myocardial Segmentation for Cardiac MRIed fully supervised techniques such as Dictionary Learning and Atlas-based techniques. But, the benefits of unsupervised techniques e.g., no need for large amount of training data and better potential of handling variability in anatomy and image contrast, is more evident with emerging cardiac MR mod

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倔強不能

Slic-Seg: Slice-by-Slice Segmentation Propagation of the Placenta in Fetal MRI Using One-Plane Scribuality due to sparse acquisition, inter-slice motion, and the widely varying position and orientation of the placenta between pregnant women. We propose a minimally interactive online learning-based method named Slic-Seg to obtain accurate placenta segmentations from MRI. An online random forest is

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Chronological

Multi-Level Parcellation of the Cerebral Cortex Using Resting-State fMRIts at developing parcellation algorithms using resting-state fMRI, there still remain challenges to be overcome, such as generating reproducible parcellations at both single-subject and group levels, while sub-dividing the cortex into functionally homogeneous parcels. To address these challenges, we

RADE

Interactive Multi-organ Segmentation Based on Multiple Template Deformationof [1] with user-provided hard constraints that can be optimized globally or locally, we propose an efficient and user-friendly solution that ensures consistent feedback to the user interactions. We demonstrate the potential of our approach through a user study with 10 medical imaging experts, aimin