Optimized PatchMatch for Near Real Time and Accurate Label Fusion - Archive ouverte HAL Access content directly
Conference Papers Year : 2014

Optimized PatchMatch for Near Real Time and Accurate Label Fusion

Abstract

Automatic segmentation methods are important tools for quantitative analysis of Magnetic Resonance Images. Recently, patch- based label fusion approaches demonstrated state-of-the-art segmenta- tion accuracy. In this paper, we introduce a new patch-based method using the PatchMatch algorithm to perform segmentation of anatomical structures. Based on an Optimized PAtchMatch Label fusion (OPAL) strategy, the proposed method provides competitive segmentation accu- racy in near real time. During our validation on hippocampus segmenta- tion of 80 healthy subjects, OPAL was compared to several state-of-the- art methods. Results show that OPAL obtained the highest median Dice coefficient (89.3%) in less than 1 sec per subject. These results highlight the excellent performance of OPAL in terms of computation time and segmentation accuracy compared to recently published methods.
Fichier principal
Vignette du fichier
Ta_MICCAI2014.pdf (461.49 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01006329 , version 1 (15-06-2014)

Identifiers

  • HAL Id : hal-01006329 , version 1

Cite

Vinh-Thong Ta, Rémi Giraud, D. Louis Collins, Pierrick Coupé. Optimized PatchMatch for Near Real Time and Accurate Label Fusion. MICCAI 2014, Sep 2014, United States. 8 p. ⟨hal-01006329⟩

Collections

CNRS
259 View
1615 Download

Share

Gmail Facebook X LinkedIn More