Multi-organ localization with cascaded global-to-local regression and shape prior.

We propose a method for fast, accurate and robust localization of several organs in medical images. We generalize the global-to-local cascade of regression random forest to multiple organs. A first regressor encodes the global relationships between organs, learning simultaneously all organs parameters. Then subsequent regressors refine the localization of each organ locally and independently for improved accuracy. By combining the regression vote distribution and the organ shape prior (through probabilistic atlas representation) we compute confidence maps that are organ-dedicated probability maps. They are used within the cascade itself, to better select the test voxels for the second set of regressors, and to provide richer information than the classical bounding boxes result thanks to the shape prior. We propose an extensive study of the different learning and testing parameters, showing both their robustness to reasonable perturbations and their influence on the final algorithm accuracy. Finally we demonstrate the robustness and accuracy of our approach by evaluating the localization of six abdominal organs (liver, two kidneys, spleen, gallbladder and stomach) on a large and diverse database of 130 CT volumes. Moreover, the comparison of our results with two existing methods shows significant improvements brought by our approach and our deep understanding and optimization of the parameters.

Early survival prediction after intra-arterial therapies: a 3D quantitative MRI assessment of tumour response after TACE or radioembolization of colorectal cancer metastases to the liver.

OBJECTIVES: This study evaluated the predictive role of 1D, 2D and 3D quantitative, enhancement-based MRI regarding overall survival (OS) in patients with colorectal liver metastases (CLM) following intra-arterial therapies (IAT). METHODS: This retrospective analysis included 29 patients who underwent transarterial chemoembolization (TACE) or radioembolization and received MRI within 6 weeks after therapy. Tumour response was assessed using 1D and 2D criteria (such as European Association for the Study of the Liver guidelines [EASL] and modified Response Evaluation Criteria in Solid Tumors [mRECIST]). In addition, a segmentation-based 3D quantification of overall (volumetric [v] RECIST) and enhancing lesion volume (quantitative [q] EASL) was performed on portal venous phase MRI. Accordingly, patients were classified as responders (R) and non-responders (NR). Survival was evaluated using Kaplan-Meier analysis and compared using Cox proportional hazard ratios (HR). RESULTS: Only enhancement-based criteria identified patients as responders. EASL and mRECIST did not predict patient survival (P = 0.27 and P = 0.44, respectively). Using uni- and multivariate analysis, qEASL was identified as the sole predictor of patient survival (9.9 months for R, 6.9 months for NR; P = 0.038; HR 0.4). CONCLUSION: The ability of qEASL to predict survival early after IAT provides evidence for potential advantages of 3D quantitative tumour analysis. KEY POINTS: • Volumetric assessment of colorectal liver metastases after intra-arterial therapy is feasible. • Early 3D quantitative tumour analysis after intra-arterial therapy may predict patient survival. • Volumetric tumour response assessment shows advantages over 1D and 2D techniques. • Enhancement-based MR response assessment is preferable to size-based measurements.

Multi-organ localization combining global-to-local regression and confidence maps.

We propose a method for fast, accurate and robust localization of several organs in medical images. We generalize global-to-local cascades of regression forests [1] to multiple organs. A first regressor encodes global relationships between organs. Subsequent regressors refine the localization of each organ locally and independently for improved accuracy. We introduce confidence maps, which incorporate information about both the regression vote distribution and the organ shape through probabilistic atlases. They are used within the cascade itself, to better select the test voxels for the second set of regressors, and to provide richer information than the classical bounding boxes thanks to the shape prior. We demonstrate the robustness and accuracy of our approach through a quantitative evaluation on a large database of 130 CT volumes.

Radiologic-pathologic analysis of contrast-enhanced and diffusion-weighted MR imaging in patients with HCC after TACE: diagnostic accuracy of 3D quantitative image analysis.

PURPOSE: To evaluate the diagnostic performance of three-dimensional ( 3D three-dimensional ) quantitative enhancement-based and diffusion-weighted volumetric magnetic resonance (MR) imaging assessment of hepatocellular carcinoma ( HCC hepatocellular carcinoma ) lesions in determining the extent of pathologic tumor necrosis after transarterial chemoembolization ( TACE transarterial chemoembolization ). MATERIALS AND METHODS: This institutional review board-approved retrospective study included 17 patients with HCC hepatocellular carcinoma who underwent TACE transarterial chemoembolization before surgery. Semiautomatic 3D three-dimensional volumetric segmentation of target lesions was performed at the last MR examination before orthotopic liver transplantation or surgical resection. The amount of necrotic tumor tissue on contrast material-enhanced arterial phase MR images and the amount of diffusion-restricted tumor tissue on apparent diffusion coefficient ( ADC apparent diffusion coefficient ) maps were expressed as a percentage of the total tumor volume. Visual assessment of the extent of tumor necrosis and tumor response according to European Association for the Study of the Liver ( EASL European Association for the Study of the Liver ) criteria was performed. Pathologic tumor necrosis was quantified by using slide-by-slide segmentation. Correlation analysis was performed to evaluate the predictive values of the radiologic techniques. RESULTS: At histopathologic examination, the mean percentage of tumor necrosis was 70% (range, 10%-100%). Both 3D three-dimensional quantitative techniques demonstrated a strong correlation with tumor necrosis at pathologic examination (R(2) = 0.9657 and R(2) = 0.9662 for quantitative EASL European Association for the Study of the Liver and quantitative ADC apparent diffusion coefficient , respectively) and a strong intermethod agreement (R(2) = 0.9585). Both methods showed a significantly lower discrepancy with pathologically measured necrosis (residual standard error [ RSE residual standard error ] = 6.38 and 6.33 for quantitative EASL European Association for the Study of the Liver and quantitative ADC apparent diffusion coefficient , respectively), when compared with non- 3D three-dimensional techniques ( RSE residual standard error = 12.18 for visual assessment). CONCLUSION: This radiologic-pathologic correlation study demonstrates the diagnostic accuracy of 3D three-dimensional quantitative MR imaging techniques in identifying pathologically measured tumor necrosis in HCC hepatocellular carcinoma lesions treated with TACE transarterial chemoembolization .

Three-dimensional evaluation of lipiodol retention in HCC after chemoembolization: a quantitative comparison between CBCT and MDCT.

RATIONALE AND OBJECTIVES: To evaluate the capability of cone-beam computed tomography (CBCT) acquired immediately after transcatheter arterial chemoembolization (TACE) in determining lipiodol retention quantitatively and volumetrically when compared to 1-day postprocedure unenhanced multidetector computed tomography (MDCT). MATERIALS AND METHODS: From June to December 2012, 15 patients met the inclusion criteria of unresectable hepatocellular carcinoma (HCC) that was treated with conventional TACE (cTACE) and had intraprocedural CBCT and 1-day post-TACE MDCT. Four patients were excluded because the lipiodol was diffuse throughout the entire liver or lipiodol deposition was not clear on both CBCT and MDCT. Eleven patients with a total of 31 target lesions were included in the analysis. A quantitative three-dimensional software was used to assess complete, localized, and diffuse lipiodol deposition. Tumor volume, lipiodol volume in the tumor, percent lipiodol retention, and lipiodol enhancement in Hounsfield units (HU) were calculated and compared between CBCT and MDCT using two-tailed Student's t test and Bland-Altman plots. RESULTS: The mean value of tumor volume, lipiodol-deposited regions, calculated average percent lipiodol retention, and HU value of CBCT were not significantly different from those of MDCT (tumor volume: 9.37 ± 11.35 cm(3) vs 9.34 ± 11.44 cm(3), P = .991; lipiodol volume: 7.84 ± 9.34 cm(3) vs 7.84 ± 9.60 cm(3), P = .998; lipiodol retention: 89.3% ± 14.7% vs. 90.2% ± 14.9%, P = .811; HU value: 307.7 ± 160.1 HU vs. 257.2 ± 120.0 HU, P = .139). Bland-Altman plots showed only minimal difference and high agreement when comparing CBCT to MDCT. CONCLUSIONS: CBCT has a similar capability, intraprocedurally, to assess lipiodol deposition in three dimensions for patients with HCC treated with cTACE when compared to MDCT.

Automatic detection and segmentation of kidneys in 3D CT images using random forests.

Kidney segmentation in 3D CT images allows extracting useful information for nephrologists. For practical use in clinical routine, such an algorithm should be fast, automatic and robust to contrast-agent enhancement and fields of view. By combining and refining state-of-the-art techniques (random forests and template deformation), we demonstrate the possibility of building an algorithm that meets these requirements. Kidneys are localized with random forests following a coarse-to-fine strategy. Their initial positions detected with global contextual information are refined with a cascade of local regression forests. A classification forest is then used to obtain a probabilistic segmentation of both kidneys. The final segmentation is performed with an implicit template deformation algorithm driven by these kidney probability maps. Our method has been validated on a highly heterogeneous database of 233 CT scans from 89 patients. 80% of the kidneys were accurately detected and segmented (Dice coefficient > 0.90) in a few seconds per volume.

A review of 3D vessel lumen segmentation techniques: models, features and extraction schemes.

Vascular diseases are among the most important public health problems in developed countries. Given the size and complexity of modern angiographic acquisitions, segmentation is a key step toward the accurate visualization, diagnosis and quantification of vascular pathologies. Despite the tremendous amount of past and on-going dedicated research, vascular segmentation remains a challenging task. In this paper, we review state-of-the-art literature on vascular segmentation, with a particular focus on 3D contrast-enhanced imaging modalities (MRA and CTA). We structure our analysis along three axes: models, features and extraction schemes. We first detail model-based assumptions on the vessel appearance and geometry which can embedded in a segmentation approach. We then review the image features that can be extracted to evaluate these models. Finally, we discuss how existing extraction schemes combine model and feature information to perform the segmentation task. Each component (model, feature and extraction scheme) plays a crucial role toward the efficient, robust and accurate segmentation of vessels of interest. Along each axis of study, we discuss the theoretical and practical properties of recent approaches and highlight the most advanced and promising ones.

Bayesian maximal paths for coronary artery segmentation from 3D CT angiograms.

We propose a recursive Bayesian model for the delineation of coronary arteries from 3D CT angiograms (cardiac CTA) and discuss the use of discrete minimal path techniques as an efficient optimization scheme for the propagation of model realizations on a discrete graph. Design issues such as the definition of a suitable accumulative metric are analyzed in the context of our probabilistic formulation. Our approach jointly optimizes the vascular centerline and associated radius on a 4D space+scale graph. It employs a simple heuristic scheme to dynamically limit scale-space exploration for increased computational performance. It incorporates prior knowledge on radius variations and derives the local data likelihood from a multiscale, oriented gradient flux-based feature. From minimal cost path techniques, it inherits practical properties such as computational efficiency and workflow versatility. We quantitatively evaluated a two-point interactive implementation on a large and varied cardiac CTA database. Additionally, results from the Rotterdam Coronary Artery Algorithm Evaluation Framework are provided for comparison with existing techniques. The scores obtained are excellent (97.5% average overlap with ground truth delineated by experts) and demonstrate the high potential of the method in terms of robustness to anomalies and poor image quality.

An automated image-processing strategy to analyze dynamic arterial spin labeling perfusion studies. Application to human skeletal muscle under stress.

Arterial spin labeling (ASL) perfusion measurements allow the follow-up of muscle perfusion with high temporal resolution during a stress test. Automated image processing is proposed to estimate perfusion maps from ASL images. It is based on two successive analyses: at first, automated rejection of the image pairs between which a large displacement is detected is performed, followed by factor analysis of the dynamic data and cluster analysis to classify pixels with large signal variation characteristic of vessels. Then, after masking these "vascular" pixels, factor analysis and cluster analysis are further applied to separate the different muscles between low or high perfusion increase, yielding a functional map of the leg. Data from 10 subjects (five normal volunteers and five elite sportsmen) had been analyzed. Resulting time perfusion curves from a region of interest (ROI) in active muscles show a good accordance whether extracted with automated processing or with manual processing. This method of functional segmentation allows automated suppression of vessels and fast visualization of muscles with high, medium or low perfusion, without any a priori knowledge.