Dataset Information

Lesion identification using unified segmentation-normalisation models and fuzzy clustering.

ABSTRACT: In this paper, we propose a new automated procedure for lesion identification from single images based on the detection of outlier voxels. We demonstrate the utility of this procedure using artificial and real lesions. The scheme rests on two innovations: First, we augment the generative model used for combined segmentation and normalization of images, with an empirical prior for an atypical tissue class, which can be optimised iteratively. Second, we adopt a fuzzy clustering procedure to identify outlier voxels in normalised gray and white matter segments. These two advances suppress misclassification of voxels and restrict lesion identification to gray/white matter lesions respectively. Our analyses show a high sensitivity for detecting and delineating brain lesions with different sizes, locations, and textures. Our approach has important implications for the generation of lesion overlap maps of a given population and the assessment of lesion-deficit mappings. From a clinical perspective, our method should help to compute the total volume of lesion or to trace precisely lesion boundaries that might be pertinent for surgical or diagnostic purposes.

SUBMITTER: Seghier ML

PROVIDER: S-EPMC2724121 | biostudies-literature | 2008 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Lesion identification using unified segmentation-normalisation models and fuzzy clustering.

Seghier Mohamed L ML Ramlackhansingh Anil A Crinion Jenny J Leff Alexander P AP Price Cathy J CJ

NeuroImage 20080328 4

In this paper, we propose a new automated procedure for lesion identification from single images based on the detection of outlier voxels. We demonstrate the utility of this procedure using artificial and real lesions. The scheme rests on two innovations: First, we augment the generative model used for combined segmentation and normalization of images, with an empirical prior for an atypical tissue class, which can be optimised iteratively. Second, we adopt a fuzzy clustering procedure to identi ...[more]

PMID: 18482850

Similar Datasets

Project description:PurposeIntra-procedural monitoring and post-procedural follow-up is necessary for a successful ablation treatment. An imaging technique which can assess the ablation geometry accurately is beneficial to monitor and evaluate treatment. In this study, we developed an automated ablation segmentation technique for serial low-dose, noisy ablation computed tomography (CT) or contrast-enhanced CT (CECT).MethodsLow-dose, noisy temporal CT and CECT volumes were acquired during microwave ablation on normal porcine liver (four with non-contrast CT and eight with CECT). Highly constrained backprojection (HYPR) processing was used to recover ablation zone information compromised by low-dose noise. First-order statistic features and normalized fractional Brownian features (NBF) were used to segment ablation zones by fuzzy c-mean clustering. After clustering, the segmented ablation zone was refined by cyclic morphological processing. Automatic and manual segmentations were compared to gross pathology with Dice's coefficient (morphological similarity), while cross-sectional dimensions were compared by percent difference.ResultsAutomatic and manual segmentations of the ablation zone were very similar to gross pathology (Dice Coefficients: Auto.-Path. = 0.84 ± 0.02; Manu.-Path. = 0.76 ± 0.03, P = 0.11). The differences in ablation area, major diameter and minor diameter were 17.9 ± 3.2%, 11.1 ± 3.2% and 16.2 ± 3.4%, respectively, when comparing automatic segmentation to gross pathology, which were lower than the differences of 32.9 ± 16.8%, 13.0 ± 9.8% and 21.8 ± 5.8% when comparing manual segmentation to gross pathology. Manual segmentations tended to overestimate gross pathology when ablation area was less than 15 cm2 , but the automated segmentation tended to underestimate gross pathology when ablation zone is larger than 20 cm2 .ConclusionFuzzy c-means clustering may be used to aid automatic segmentation of ablation zones without prior information or user input, making serial CT/CECT has more potential to assess treatments intra-procedurally.

Project description:BACKGROUND: Extensive and automated data integration in bioinformatics facilitates the construction of large, complex biological networks. However, the challenge lies in the interpretation of these networks. While most research focuses on the unipartite or bipartite case, we address the more general but common situation of k-partite graphs. These graphs contain k different node types and links are only allowed between nodes of different types. In order to reveal their structural organization and describe the contained information in a more coarse-grained fashion, we ask how to detect clusters within each node type. RESULTS: Since entities in biological networks regularly have more than one function and hence participate in more than one cluster, we developed a k-partite graph partitioning algorithm that allows for overlapping (fuzzy) clusters. It determines for each node a degree of membership to each cluster. Moreover, the algorithm estimates a weighted k-partite graph that connects the extracted clusters. Our method is fast and efficient, mimicking the multiplicative update rules commonly employed in algorithms for non-negative matrix factorization. It facilitates the decomposition of networks on a chosen scale and therefore allows for analysis and interpretation of structures on various resolution levels. Applying our algorithm to a tripartite disease-gene-protein complex network, we were able to structure this graph on a large scale into clusters that are functionally correlated and biologically meaningful. Locally, smaller clusters enabled reclassification or annotation of the clusters' elements. We exemplified this for the transcription factor MECP2. CONCLUSIONS: In order to cope with the overwhelming amount of information available from biomedical literature, we need to tackle the challenge of finding structures in large networks with nodes of multiple types. To this end, we presented a novel fuzzy k-partite graph partitioning algorithm that allows the decomposition of these objects in a comprehensive fashion. We validated our approach both on artificial and real-world data. It is readily applicable to any further problem.

Dataset Information

Lesion identification using unified segmentation-normalisation models and fuzzy clustering.

Publications

Lesion identification using unified segmentation-normalisation models and fuzzy clustering.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets