Abstract. 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-theart 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 coarseto-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.

Purpose To demonstrate that hepatic tumor volume and enhancement pattern measurements can be obtained in a time efficient and reproducible manner on a voxel-by-voxel basis to provide a true 3D volumetric assessment. Materials and Methods Retrospective evaluation of MRI data obtained from 20 patients recruited for a single-institution prospective study. All patients carried a diagnosis of hepatocellular carcinoma (HCC) and underwent drug-eluting beads transcatheter arterial chemoembolization (DEB-TACE) for the first time. All patients had undergone contrast-enhanced MRI before and after DEB-TACE although poor image quality excluded 3 resulting in a final count of 17 patients. vRECIST and qEASL were measured and segmentation and processing times were recorded. Results Thirty-four scans were analyzed. The time for semi-automatic segmentation was 65±33 seconds with a range of 40–200 seconds. vRECIST and qEASL of each tumor were then computed less than one minute for each. Conclusion Semi-automatic quantitative tumor enhancement (qEASL) and volume (vRECIST) assessment is feasible in a workflow efficient time frame. Clinical correlation is necessary, but vRECIST and qEASL could become part of the assessment of intra-arterial therapy for interventional radiologists.

3D-US demonstrated an acceptable reproducibility and a good agreement with 3D-CT, and has the potential to improve future AAA management through more reliable ultrasound guided size estimates.

Rationale and Objectives To evaluate the precision and reproducibility of a semi-automatic tumor segmentation software in measuring tumor volume of hepatocellular-carcinoma–(HCC) before the first trans-arterial chemo-embolization–(TACE) on contrast-enhancement magnetic-resonance-imaging–(CE-MRI) and intra-procedural dual-phase C-arm cone-beam computed-tomography–(DP-CBCT) images. Materials and Methods 19HCCs were targeted in 19patients(one per patient) who underwent baseline diagnostic CE-MRI and an intra-procedural DP-CBCT. The images were obtained from CE-MRI–(arterial-phase of an intra-venous contrast medium injection) and DP-CBCT–(delayed-phase of an intra-arterial contrast medium injection) before the actual embolization. Three readers measured tumor volumes using a semi-automatic 3D-volumetric segmentation software which used a region-growing method employing non-Euclidean radial basis functions. Segmentation time and spatial position were recorded. The tumor volume measurements between images sets were compared using linear-regression and Student t-test, and evaluated with Intraclass-Correlation analysis–(ICC). The inter-rater Dice Similarity Coefficient–(DSC) accessed the segmentation spatial localization. Results All 19 HCCs were analyzed. On CE-MRI and DP-CBCT examinations respectively, A) the mean segmented tumor volumes was 87±8cm3[2–873] and 92±10cm3[1–954], with no statistical difference of segmented volumes by readers of each tumor between the two imaging modalities and the mean time required for segmentation was 66±45seconds [21–173] and 85±34seconds[17–214] (p=0.19), B),the ICCs were 0.99 and 0.974, showing a strong correlation among readers, and C) the inter-rater DSCs showed a good to excellent inter-user agreement on the spatial localization of the tumor segmentation–(0.70±0.07 and 0.74±0.05,p=0.07). Conclusion This study shows a strong correlation, precision and reproducibility of semi-automatic tumor segmentation software in measuring tumor volume on CE-MRI and DP-CBCT images. The use of the segmentation software on DP-CBCT and CE-MRI can be a valuable and highly accurate tool to measure the volume of hepatic tumors.

Abstract. In this paper we consider a new approach for single object segmentation in 3D images. Our method improves the classical geodesic active surface model. It greatly simplifies the model initialization and naturally avoids local minima by incorporating user extra information into the segmentation process. The initialization procedure is reduced to introducing 3D curves into the image. These curves are supposed to belong to the surface to extract and thus, also constitute user given information. Hence, our model finds a surface that has these curves as boundary conditions and that minimizes the integral of a potential function that corresponds to the image features. Our goal is achieved by using globally minimal paths. We approximate the surface to extract by a discrete network of paths. Furthermore, an interpolation method is used to build a mesh or an implicit representation based on the information retrieved from the network of paths. Our paper describes a fast construction obtained by exploiting the Fast Marching algorithm and a fast analytical interpolation method. Moreover, a Level set method can be used to refine the segmentation when higher accuracy is required. The algorithm has been successfully applied to 3D medical images and synthetic images.