Fast and Memory Efficient Segmentation of Lung Tumors Using Graph Cuts

12In medical imaging, segmenting accurately lung tumors stay a quite challenging task when touching directly with healthy tissues. In this paper, we address the problem of extracting interactively these tumors with graph cuts. The originality of this work consists in (1) reducing input graphs to reduce resource consumption when segmenting large volume data and (2) introducing a novel energy formulation to inhibit the propagation of the object seeds. We detail our strategy to achieve relevant segmentations of lung tumors and compare our results to hand made segmentations provided by an expert. Comprehensive experiments show how our method can get solutions near from ground truth in a fast and memory efficient way

Threshold selection, mitosis and dual mutation in cooperative co-evolution: application to medical 3d tomography

International audienceWe present and analyse the behaviour of specialised operators designed for cooperative coevolution strategy in the framework of 3D tomographic PET reconstruction. The basis is a simple cooperative co-evolution scheme (the "fly algorithm"), which embeds the searched solution in the whole population, letting each individual be only a part of the solution. An individual, or fly, is a 3D point that emits positrons. Using a cooperative co-evolution scheme to optimize the position of positrons, the population of flies evolves so that the data estimated from flies matches measured data. The final population approximates the radioactivity concentration. In this paper, three operators are proposed, threshold selection, mitosis and dual mutation, and their impact on the algorithm efficiency is experimentally analysed on a controlled test-case. Their extension to other cooperative co-evolution schemes is discussed

Une méthode de réduction exacte pour la segmentation par graph cuts

8 pagesLes graph cuts sont désormais un standard au sein de la communauté de la vision par ordinateur. Néanmoins, leur grande consommation mémoire reste un problème majeur : les graphes sous-jacents contiennent des milliards de noeuds et davantage d'arcs. Excepté quelques méthodes [14, 10, 5] exactes, les heuristiques présentes dans la littérature ne permettent d'obtenir qu'une solution approchée [12, 8]. Dans un premier temps, nous présentons une nouvelle stratégie pour réduire exactement ces graphes : le graphe est construit en ajoutant les noeuds qui satisfont localement une condition donnée et correspond à une bande étroite autour des contours de l'objet à segmenter. Les expériences présentées pour segmenter des images en niveaux de gris et en couleur mettent en évidence une faible consommation mémoire tout en garantissant une faible distance sur les segmentations. Nous présentons aussi une application de cette méthode pour segmenter des tumeurs dans des images scanner

Voxelisation in the 3-D Fly Algorithm for PET

International audienceThe Fly Algorithm was initially developed for 3-D robot vision applications. It consists in solving the inverse problem of shape reconstruction from projections by evolving a population of 3-D points in space (the 'flies'), using an evolutionary optimisation strategy. Here, in its version dedicated to tomographic reconstruction in medical imaging, the flies are mimicking radioactive photon sources. Evolution is controlled using a fitness function based on the discrepancy of the projections simulated by the flies with the actual pattern received by the sensors. The reconstructed radioactive concentration is derived from the population of flies, i.e. a collection of points in the 3-D Euclidean space, after convergence. 'Good' flies were previously binned into voxels. In this paper, we study which flies to include in the final solution and how this information can be sampled to provide more accurate datasets in a reduced computation time. We investigate the use of density fields, based on Metaballs and on Gaussian functions respectively, to obtain a realistic output. The spread of each Gaussian kernel is modulated in function of the corresponding fly fitness. The resulting volumes are compared with previous work in terms of normalised-cross correlation. In our test-cases, data fidelity increases by more than 10% when density fields are used instead of binning. Our method also provides reconstructions comparable to those obtained using well-established techniques used in medicine (filtered back-projection and ordered subset expectation-maximisation)

3D superimposition of CT and SPECT images of the brain by a mechanical based method

Also in European Journal of Nuclear Medicine 19(8):624no abstrac

Artificial evolution for PET and SPECT reconstruction

International audienceWe propose an evolutionary approach for image reconstruction in nuclear medicine. Our method is based on a cooperative coevolution strategy (also called Parisian evolution): the "fly algorithm". Method and Materials: Each individual, or fly, corresponds to a 3D point that mimics a radioactive emitter, i.e. a stochastic simulation of annihilation events is performed to compute the fly's illumination pattern. For each annihilation, a photon is emitted in a random direction, and a second photon is emitted in the opposite direction. The line between two detected photons is called line of response (LOR). If both photons are detected by the scanner, the fly's illumination pattern is updated. The LORs of every fly are aggregated to form the population total illumination pattern. Using genetic operations to optimize the position of positrons, the population of flies evolves so that the population total pattern matches measured data. The final population of flies approximates the radioactivity concentration. Results: We have developed numerical phantom models to assess the reconstruction algorithm. To date, no scattering and no tissue attenuation have been considered. Whilst this is not physically correct, it allows us to test and validate our approach in the simplest cases. Preliminary results show the validity of this approach in both 2D and fully-3D modes. In particular, the size of objects, and their relative concentrations can be retrieved in the 2D mode. In fully-3D, complex shapes can be reconstructed. Conclusions: An evolutionary approach for PET reconstruction has been proposed and validated using simple test cases. Further work will therefore include the use of more realistic input data (including random events and scattering), which will finally lead to implement the correction of scattering within our algorithm. A comparison study against ML-EM and/or OS-EM methods will also need to be conducted

3D superimposition of CT and SPECT images of the brain by a mechanical based method

Also in European Journal of Nuclear Medicine 19(8):624no abstrac

Registration of 3D medical images using a mechanical based method

no abstrac

Matching Of 3D Medical Images With A Potential Based Method

Three dimensional (3D) images take an increasing importance in the medical imaging field. They may be produced by X-ray Computed Tomography (CT), Magnetic Resonance Imaging #MRI# or Positon Emitting Tomography #PET# for example. They usually contain complementary informations which have to be combined together in order to be useful to physicians. Combining the information of several 3D images sets the problem of the registration of these images. It arises when two images of the same modality are taken with different positions or taken at different times #before and after a surgery for example# or with two images of different modalities. We present a new method for computing the rigid transformation between two 3D objects. Our method looks like a gradient method that minimizes the distance between both objects, but is more powerful because it is in fact an application of the fundamental laws of dynamics which uses both #rst and second temporal derivatives of positions and allows an intuitive control of the minimization process. We show and discuss synthetic examples as well as real examples involving the registration of CT or MRI data of the head and the registration of PET and MRI images of the brain