Evaluation of state-of-the-art segmentation algorithms for left ventricle infarct from late Gadolinium enhancement MR images

Studies have demonstrated the feasibility of late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging for guiding the management of patients with sequelae to myocardial infarction, such as ventricular tachycardia and heart failure. Clinical implementation of these developments necessitates a reproducible and reliable segmentation of the infarcted regions. It is challenging to compare new algorithms for infarct segmentation in the left ventricle (LV) with existing algorithms. Benchmarking datasets with evaluation strategies are much needed to facilitate comparison. This manuscript presents a benchmarking evaluation framework for future algorithms that segment infarct from LGE CMR of the LV. The image database consists of 30 LGE CMR images of both humans and pigs that were acquired from two separate imaging centres. A consensus ground truth was obtained for all data using maximum likelihood estimation. Six widely-used fixed-thresholding methods and five recently developed algorithms are tested on the benchmarking framework. Results demonstrate that the algorithms have better overlap with the consensus ground truth than most of the n-SD fixed-thresholding methods, with the exception of the FullWidth-at-Half-Maximum (FWHM) fixed-thresholding method. Some of the pitfalls of fixed thresholding methods are demonstrated in this work. The benchmarking evaluation framework, which is a contribution of this work, can be used to test and benchmark future algorithms that detect and quantify infarct in LGE CMR images of the LV. The datasets, ground truth and evaluation code have been made publicly available through the website: https://www.cardiacatlas.org/web/guest/challenges

Hierarchical adaptive texture conditional random field for enhanced pathology segmentation

This thesis proposes a new hierarchical Conditional Random Field (CRF) based classifier to address the task of small enhanced pathology segmentation. Specifically, a Hierarchical Adaptive Texture CRF (HAT-CRF) is developed and applied to the challenging problem of Gad-enhancing lesion segmentation in brain Magnetic Resonance Images (MRI) of patients with Multiple Sclerosis (MS). In this context, aside from the general small sizes of the enhanced lesions, the presence of many non-lesional enhancements (such as blood vessels) renders the problem more difficult. The proposed HAT-CRF model is the first automatic segmentation and detection approach for this context. In addition to voxel-wise cliques of up to size three, it exploits multiple higher order textures to discriminate the true lesional enhancements from the pool of other enhancements. Moreover, a temporal model referred to as temporal Hierarchical Adaptive Texture CRF (THAT-CRF) is also developed in this thesis which is the first work addressing the incorporation of temporal texture analysis in order to study the textures of enhanced candidates over time. Since the temporal profiles of the lesional and non-lesional enhancements are different from each other, including the temporal texture comparison increases the discrimination power of the THAT-CRF model. The two proposed models are trained on very large multi-center clinical trials consisting of 2380 scans from 247 different centers as part of multi-center clinical trials. The models are further tested on two separate clinical trials consisting of 813 scans from 27 centers and 2770 scans from 142 centers. It is shown that the incorporation of the temporal textures results in a general decrease of the false detection rate. Specifically, the THAT-CRF model achieves an over all sensitivity of 95% which ranges from 89% for very small lesions (3-5 voxels) to 100% for very large ones (101+ voxels) while the average false positive count per scan ranges from 0.27 to 0. The significance of different components of the model as well as the effect of different design choices, such as the neighborhood factorization, the registration techniques, and the parameter learning and inference approaches, are studied. Comparison with Support Vector Machine (SVM), Random Forest and variant of an MRF is also included where they are all outperformed by the proposed approach. Finally, superior performance is achieved by the reviewed labelings of the proposed model (corrected only for the false detections) compared to the fully manual labeling when applied to the context of separating different treatment arms in a real clinical trial.Cette thèse propose un nouveau classificateur basé sur la méthode de Champ Aléatoire Conditionnel (CAC) hiérarchique pour la segmentation de petite pathologie augmentée. Plus précisément, un CAC Hiérarchique de Texture Adaptive (CAC-HTA) est développé et appliqué sur le problème difficile de segmentation de lésions cérébrales augmentée par gadolinium dans les images à résonance magnétique (IRM) des patients atteints de Sclérose En Plaques (SEP). Dans ce contexte, en plus de la petite taille habituelle des lésions augmentées, la présence de nombreuses augmentations non lésionnelles (tels que les vaisseaux sanguins) rend le problème plus difficile. Le modèle CAC-HTA proposé est la première approche de segmentation et de détection automatiques pour ce contexte. En plus de groupements voxelliques allant jusqu'à de taille trois, elle exploite des textures multiples d'ordre supérieures pour discriminer les véritables augmentations lésionnelles des augmentations parasites. En outre, un modèle temporel appelé CAC Temporelle Hiérarchique de Texture Adaptative (THTA-CAC) est également développé dans cette thèse. C'est la première contribution qui s'adresse à l'incorporation de l'analyse temporelle des textures pour étudier les textures des candidats augmentés dans le temps. Etant donné que les profils temporels des augmentations lésionnelles et non lésionnelles sont différents l'un de l'autre, y compris la comparaison de texture temporelle augmente le pouvoir de discrimination du modèle THTA-CRF. Les deux modèles proposés sont formés sur de très grands essais cliniques multi centres constitués de 2380 scans de 247 centres différents dans le cadre d'essais cliniques multi centriques. Les modèles sont ensuite testés sur deux essais cliniques distincts composés de 813 scans de 27 centres et 2770 scans de 142 centres. Il est démontré que l'incorporation des textures temporelles en résulte une diminution générale du taux de fausse détection. Plus précisément, le modèle THTA-CAC réalise un taux global de sensibilité de 95% qui varie de 89% pour les très petites lésions (3-5 voxels) à 100% pour les très grandes (101+ voxels), tandis que le nombre de faux positifs moyens par scan varie de 0,27 à 0. L'importance des différentes composantes du modèle ainsi que l'effet de différents choix de conception, telles que la factorisation du voisinage, les techniques de recalage et les paramètres d'apprentissage et approches d'inférence sont étudiés. Une comparaison avec les Machines à Support de Vecteurs (MSV), les Forêts Aléatoires et variantes des MRF est également inclus, dans laquelle ils sont tous surpassé par l'approche proposée. Enfin, une performance supérieure est réalisée par le marquage supervisé du modèle proposé (corrigé uniquement pour les fausses détections) par rapport au marquage entièrement manuel lorsqu'il est appliqué au contexte de séparation des différents groupes de traitement dans un véritable essai clinique

Quantification of Edematic Effects in Prostate Brachytherapy Interventions

Abstract. We present a quantitative model to analyze the detrimental effects of edema on the quality of prostate brachytherapy implants. We account for both tissue expansion and implant migration by mapping intra-operative ultrasound and post-implant CT. We pre-process the ultrasound with a phase congruency filter, and map it to the volume CT using a B-spline deformable mutual information similarity metric. To test the method, we implanted a standard training phantom with 48 seeds, imaged the phantom with ultrasound and CT and registered the two for ground truth. Edema was simulated by distorting the CT volume by known transformations. The objective was to match the distorted implant to the intra-operative ultrasound. Performance was measured relative to ground truth. We successfully mapped 100% of deformed seeds to ground truth under edematic expansion up to 40% of volume growth. Seed matching performance was 98% with random seed migration of 3mm superimposed on 10% edematic volume growth. This method promises to be clinically applicable as the first quantitative analysis tool to measure edematic implant deformations occurring between the operating room and post-operative CT imaging

Localization of brachytherapy seeds in ultrasound by registration to fluoroscopy.

Motivation: In prostate brachytherapy, transrectal ultrasound (TRUS) is used to visualize the anatomy, while implanted seeds can be seen in C-arm fluoroscopy or CT. Intra-operative dosimetry optimization requires localization of the implants in TRUS relative to the anatomy. This could be achieved by registration of TRUS images and the implants reconstructed from fluoroscopy or CT. Methods: TRUS images are filtered, compounded, and registered on the reconstructed implants by using an intensity-based metric based on a 3D point-to-volume registration scheme. A phantom was implanted with 48 seeds, imaged with TRUS and CT/X-ray. Ground-truth registration was established between the two. Seeds were reconstructed from CT/X-ray. Seven TRUS filtering techniques and two image similarity metrics were analyzed as well. Results: For point-to-volume registration, noise reduction combined with beam profile filter and mean squares metrics yielded the best result: an average of 0.38 ± 0.19 mm seed localization error relative to the ground-truth. In human patient data C-arm fluoroscopy images showed 81 radioactive seeds implanted inside the prostate. A qualitative analysis showed clinically correct agreement between the seeds visible in TRUS and reconstructed from intra-operative fluoroscopy imaging. The measured registration error compared to the manually selected seed locations by the clinician was 2.86 ± 1.26 mm. Conclusion: Fully automated seed localization in TRUS performed excellently on ground-truth phantom, adequate in clinical data and was time efficient having an average runtime of 90 seconds. Copyright 2010 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofReviewedFacult