Conference Paper

Adaptive segmentation of internal brain structures in pathological MR images depending on tumor types

CNRS, Ecole Nat. Superieure des Telecommun., Paris
DOI: 10.1109/ISBI.2007.356920 Conference: Biomedical Imaging: From Nano to Macro, 2007. ISBI 2007. 4th IEEE International Symposium on
Source: IEEE Xplore


This paper introduces a novel methodology for the segmentation of internal brain structures in MRI volumes in the presence of a tumor. The proposed method relies on an initial segmentation of the tumor. Based on the tumor's type, a set of spatial relations between internal structures, remaining stable even in presence of the pathology, is established. Segmentation and recognition of surrounding anatomical structures are based on prior knowledge about their spatial arrangement. Segmentation results on tumors inducing small or large deformations are provided to illustrate the potential of the approach.

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Available from: Jamal Atif,
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    • "As shown in Refs. [21] [22], spatial relations improve the robustness of the segmentation of the structures even in the presence of pathologies. Another class of methods segment multiple objects simultaneously. "
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    • "For instance, tumors inducing little deformation will not lead to a modification of the spatial relations used for the segmentation with respect to the ones used in the normal cases. On the contrary, tumors inducing strong deformations will lead to adaptation of the spatial relations, especially metrical ones [31]. "
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    ABSTRACT: Sequential methods for knowledge-based recognition of structures re-quire to define in which order the structures have to be recognized. We propose to address this problem by integrating pre-attention mech-anisms, in the form of a saliency map, in the determination of the or-der. As pre-attention mechanisms extract knowledge from an image without object recognition in ad-vance and do not require any a pri-ori knowledge on the image, they provide useful knowledge for guid-ing object segmentation and recog-nition. Additionally, we make use of generic knowledge of the scene, expressed as spatial relations, since they play a crucial role in model-based image recognition and inter-pretation due to their stability com-pared to many other image appear-ance characteristics. Graphs are well adapted to represent this infor-mation, and finding an order then amounts to find a path in a graph. The proposed algorithms are applied on brain image understanding.
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