Medical image registration using Edgeworth-based approximation of Mutual Information

International audienceWe propose a new similarity measure for iconic medical image registration, an Edgeworth-based third order approximation of Mutual Information (MI) and named 3-EMI. Contrary to classical Edgeworth-based MI approximations, such as those proposed for inde- pendent component analysis, the 3-EMI measure is able to deal with potentially correlated variables. The performance of 3-EMI is then evaluated and compared with the Gaussian and B-Spline kernel-based estimates of MI, and the validation is leaded in three steps. First, we compare the intrinsic behavior of the measures as a function of the number of samples and the variance of an additive Gaussian noise. Then, they are evaluated in the context of multimodal rigid registration, using the RIRE data. We finally validate the use of our measure in the context of thoracic monomodal non-rigid registration, using the database proposed during the MICCAI EMPIRE10 challenge. The results show the wide range of clinical applications for which our measure can perform, including non-rigid registration which remains a challenging problem. They also demonstrate that 3-EMI outperforms classical estimates of MI for a low number of samples or a strong additive Gaussian noise. More generally, our measure gives competitive registration results, with a much lower numerical complexity compared to classical estimators such as the reference B-Spline kernel estimator, which makes 3-EMI a good candidate for fast and accurate registration tasks

Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning

Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically relevant tasks. This limits the development of registration methods, the adoption of research advances into practice, and a fair benchmark across competing approaches. The Learn2Reg challenge addresses these limitations by providing a multi-task medical image registration data set for comprehensive characterisation of deformable registration algorithms. A continuous evaluation will be possible at https://learn2reg.grand-challenge.org. Learn2Reg covers a wide range of anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR), availability of annotations, as well as intra- and inter-patient registration evaluation. We established an easily accessible framework for training and validation of 3D registration methods, which enabled the compilation of results of over 65 individual method submissions from more than 20 unique teams. We used a complementary set of metrics, including robustness, accuracy, plausibility, and runtime, enabling unique insight into the current state-of-the-art of medical image registration. This paper describes datasets, tasks, evaluation methods and results of the challenge, as well as results of further analysis of transferability to new datasets, the importance of label supervision, and resulting bias. While no single approach worked best across all tasks, many methodological aspects could be identified that push the performance of medical image registration to new state-of-the-art performance. Furthermore, we demystified the common belief that conventional registration methods have to be much slower than deep-learning-based methods

Approximation de l'Information Mutuelle basée sur le développement d'Edgeworth : application au recalage d'images médicales.

Mutual Information (MI) is considered as the most common similarity measure in the context of intensity-based image registration. This measure is well-known for its ability to perform tri-dimensional multimodal medical image registration. However, MI's estimators suffer from variance, bias and lead to high computational complexity. During this PhD thesis, we dealt with some statistical tools called cumulants in order to build novel approximations of MI based on Edgeworth expansion. This expansion allows one to approximate a probability density in terms of cumulants. The estimate of these approximations as similarity measure was analyzed in terms of performance on both synthetic and real data, on rigid and non-rigid medical images registration tasks. A comparison with classical estimators of MI was also performed.Dans le cadre du recalage d'images basé sur l'information d'intensité, l'Information Mutuelle (IM) est couramment utilisée comme mesure de similarité. Cette mesure est en outre particulièrement adaptée au recalage d'images médicales multimodales tri-dimensionnelles. Cependant, les estimateurs de l'IM ont en général une variance élevée et induisent des temps de calcul importants. Au cours de cette thèse, nous nous sommes intéressés aux outils statistiques que sont les cumulants pour construire de nouvelles approximations de l'IM basée sur un développement d'Edgeworth tronqué, le développement d'Edgeworth permettant d'approximerune densité de probabilité à partir de ces cumulants. L'estimée de ces approximations comme mesure de similarité a été évaluée sur données synthétiques et réelles, dans le cadre du recalage rigide et non-rigide d'images médicales multimodales et a été comparée aux estimateurs de référence de l'IM

Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge

11 pages, 4 figures, submitted to MICCAI 2019 - KiTS ChallengePrecise characterization of the kidney and kidney tumor characteristics is of outmost importance in the context of kidney cancer treatment, especially for nephron sparing surgery which requires a precise localization of the tissues to be removed. The need for accurate and automatic delineation tools is at the origin of the KiTS19 challenge. It aims at accelerating the research and development in this field to aid prognosis and treatment planning by providing a characterized dataset of 300 CT scans to be segmented. To address the challenge, we proposed an automatic, multi-stage, 2.5D deep learning-based segmentation approach based on Residual UNet framework. An ensembling operation is added at the end to combine prediction results from previous stages reducing the variance between single models. Our neural network segmentation algorithm reaches a mean Dice score of 0.96 and 0.74 for kidney and kidney tumors, respectively on 90 unseen test cases. The results obtained are promising and could be improved by incorporating prior knowledge about the benign cysts that regularly lower the tumor segmentation results

營建法學與最高行政法院裁判及決

Precise characterization of the kidney and kidney tumor characteristics is of outmost importance in the context of kidney cancer treatment, especially for nephron sparing surgery which requires a precise localization of the tissues to be removed. The need for accurate and automatic delineation tools is at the origin of the KiTS19 challenge. It aims at accelerating the research and development in this field to aid prognosis and treatment planning by providing a characterized dataset of 300 CT scans to be segmented. To address the challenge, we proposed an automatic, multi-stage, 2.5D deep learning-based segmentation approach based on Residual UNet framework. An ensembling operation is added at the end to combine prediction results from previous stages reducing the variance between single models. Our neural network segmentation algorithm reaches a mean Dice score of 0.96 and 0.74 for kidney and kidney tumors, respectively on 90 unseen test cases. The results obtained are promising and could be improved by incorporating prior knowledge about the benign cysts that regularly lower the tumor segmentation results.Comment: 11 pages, 4 figures, submitted to MICCAI 2019 - KiTS Challeng

Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge

11 pages, 4 figures, submitted to MICCAI 2019 - KiTS ChallengePrecise characterization of the kidney and kidney tumor characteristics is of outmost importance in the context of kidney cancer treatment, especially for nephron sparing surgery which requires a precise localization of the tissues to be removed. The need for accurate and automatic delineation tools is at the origin of the KiTS19 challenge. It aims at accelerating the research and development in this field to aid prognosis and treatment planning by providing a characterized dataset of 300 CT scans to be segmented. To address the challenge, we proposed an automatic, multi-stage, 2.5D deep learning-based segmentation approach based on Residual UNet framework. An ensembling operation is added at the end to combine prediction results from previous stages reducing the variance between single models. Our neural network segmentation algorithm reaches a mean Dice score of 0.96 and 0.74 for kidney and kidney tumors, respectively on 90 unseen test cases. The results obtained are promising and could be improved by incorporating prior knowledge about the benign cysts that regularly lower the tumor segmentation results

Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge

11 pages, 4 figures, submitted to MICCAI 2019 - KiTS ChallengePrecise characterization of the kidney and kidney tumor characteristics is of outmost importance in the context of kidney cancer treatment, especially for nephron sparing surgery which requires a precise localization of the tissues to be removed. The need for accurate and automatic delineation tools is at the origin of the KiTS19 challenge. It aims at accelerating the research and development in this field to aid prognosis and treatment planning by providing a characterized dataset of 300 CT scans to be segmented. To address the challenge, we proposed an automatic, multi-stage, 2.5D deep learning-based segmentation approach based on Residual UNet framework. An ensembling operation is added at the end to combine prediction results from previous stages reducing the variance between single models. Our neural network segmentation algorithm reaches a mean Dice score of 0.96 and 0.74 for kidney and kidney tumors, respectively on 90 unseen test cases. The results obtained are promising and could be improved by incorporating prior knowledge about the benign cysts that regularly lower the tumor segmentation results

Approximation de l'information mutuelle basée sur le développement d'Edgeworth (application au recalage d'images médicales)

Dans le cadre du recalage d images basé sur l information d intensité, l Information Mutuelle (IM) est couramment utilisée comme mesure de similarité. Cette mesure est en outre particulièrement adaptée au recalage d images médicales multimodales tri-dimensionnelles. Cependant, les estimateurs de l IM ont en général une variance élevée et induisent des temps de calcul importants. Au cours de cette thèse, nous nous sommes intéressés aux outils statistiques que sont les cumulants pour construire de nouvelles approximations de l IM basée sur un développement d Edgeworth tronqué, le développement d Edgeworth permettant d approximer une densité de probabilité à partir de ces cumulants. L estimée de ces approximations comme mesure de similarité a été évaluée sur données synthétiques et réelles, dans le cadre du recalage rigide et non-rigide d images médicales multimodales et a été comparée aux estimateurs de référence de l IM.Mutual Information (MI) is considered as the most common similarity measure in the context of intensity-based image registration. This measure is well-known for its ability to perform tri-dimensional multimodal medical image registration. However, MI s estimators suffer from variance, bias and lead to high computational complexity. During this PhD thesis, we dealt with some statistical tools called cumulants in order to build novel approximations of MI based on Edgeworth expansion. This expansion allows one to approximate a probability density in terms of cumulants. The estimate of these approximations as similarity measure was analyzed in terms of performance on both synthetic and real data, on rigid and nonrigid medical images registration tasks. A comparison with classical estimators of MI was also performed.RENNES1-BU Sciences Philo (352382102) / SudocSudocFranceF

Edgeworth-based approximation of Mutual Information for medical image registration

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