Atlas-Based Segmentation of Pathological MR Brain Images Using a Model of Lesion Growth

Abstract

We propose a method for brain atlas deformation in the presence of large space-occupying tumors, based on an a priori model of lesion growth that assumes radial expansion of the lesion from its starting point. Our approach involves three steps. First, an affine registration brings the atlas and the patient into global correspondence. Then, the seeding of a synthetic tumor into the brain atlas provides a template for the lesion. The last step is the deformation of the seeded atlas, combining a method derived from optical flow principles and a model of lesion growth. Results show that a good registration is performed and that the method can be applied to automatic segmentation of structures and substructures in brains with gross deformation, with important medical applications in neurosurgery, radiosurgery, and radiotherapy.

Active deformation fields: Dense deformation field estimation for atlas-based segmentation using the active contour framework

“…Moreover, the active contour is going to segment the tumor of the patient image during the registration process. Thus, the pre-segmentation of the patient tumor is not required unlike with our previous method (Bach Cuadra et al, 2004). We can see that our active contour-based algorithm allows to select the atlas contours that drive its registration.…”

“…To grow the patient's tumor in the atlas, we use a technique inspired by the tumor growth model we have previously presented in Bach Cuadra et al (2004). This technique inserts a one-voxel seed (shown by a red point in Fig.…”

Automatic segmentation framework for primary tumors from brain MRIs using morphological filtering techniques

“…Unsupervised fuzzy clustering techniques [5], Tumor Extraction by Combining k-means Clustering and Perona-Malik Anisotropic Diffusion Model [6], Brain Tumor segmentation from MRI Based on Energy Functional [7], unsupervised automatic segmentation algorithm using expectation maximization [8] technique, binary mathematical morphology [9,10], Automatic seeded region growing method [11] and Segmentation with Radix4 FFT [12] are some of the examples based on unsupervised method. Segmentation using knowledge based techniques [13], fuzzy based segmentation [14,15], segmentation using texture analysis [16], Adaptive template moderated [17] and Atlas based segmentation [18] are some of the supervised segmentation techniques. Supervised algorithms are usually very slow to train and require a lot of manually segmented data.…”

“…Problems in brain volume extraction arise [16] because there is a great deal of overlap in intensity values between the non-brain and brain tissues and because the two can often appear connected. One method to deal with these difficulties is to allow for some loss of brain tissue in a preliminary segmentation step and then to recover the tissue using morphological filters [15,18].…”

Atlas-based segmentation of medical images locally constrained by level sets

“…However this is not yet sufficient to guaranty the desired quality of segmentation for the most demanding applications. Most of the time the only constraint used on the transformation is its smoothness, ensured for instance by a Gaussian filtering [2] or constraints between interpolation functions [3]. When at some places contours are not accurate enough, it is usual to globally or locally allow more elasticity to the deformation in order to obtain a more local deformation, with the risk of increasing the irregularity of the deformation field and thus of the contours, without necessarily obtaining the sought level of precision.…”