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Renal ultrasound imaging is the primary imaging modality for the assessment of the kidney’s condition and is essential for diagnosis, treatment and surgical intervention planning, and follow-up. In this regard, kidney delineation in three-dimensional ultrasound images represents a relevant and challenging task in clinical practice. In this paper, a novel framework is proposed to accurately segment the kidney in 3D ultrasound images. The proposed framework can be divided into two stages: 1) initialization of the segmentation method; and 2) kidney segmentation. Within the initialization stage, a phase-based feature detection method is used to detect edge points at kidney boundaries, from which the segmentation is automatically initialized. In the segmentation stage, the B-Spline Explicit Active Surface framework is adapted to obtain the final kidney contour. Here, a novel hybrid energy functional that combines localized region-based and edge-based terms is used during segmentation. For the edge term, a fast signed phase-based detection approach is applied. The proposed framework was validated in two distinct datasets: (1) 15 3D challenging poor-quality ultrasound images used for experimental development, parameters assessment, and evaluation; and (2) 42 3D ultrasound images (both healthy and pathologic kidneys) used to unbiasedly assess its accuracy. Overall, the proposed method achieved a Dice overlap around 81% and an average point-to-surface error of ~2.8 mm. These results demonstrate the potential of the proposed method for clinical usage., This work was funded by project “NORTE-01-0145-FEDER-000045”, supported by Northern Portugal Regional Operational Programme (Norte2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (FEDER). This project was also funded by national funds (PIDDAC), through the FCT – Fundação para a Ciência e Tecnologia and FCT/MCTES under the scope of the project UIDB/05549/2020 and UIDP/05549/2020. The authors also acknowledge support from FCT and the European Social Found, through Programa Operacional Capital Humano (POCH), in the scope of the PhD grant SFRH/BD/136670/2018, SFRH/BD/136721/2018, and PD/BDE/113597/2015.

Deformational Plagiocephaly (DP) refers to an asymmetrical distortion of an infant’s skull resulting from external forces applied over time. The diagnosis of this condition is performed using asymmetry indexes that are estimated from specific anatomical landmarks, whose are manually defined on head models acquired using laser scans. However, this manual identification is susceptible to intra-/inter-observer variability, being also time-consuming. Therefore, automatic strategies for the identification of the landmarks and, consequently, extraction of asymmetry indexes, are claimed. A novel pipeline to automatically identify these landmarks on 3D head models and to estimate the relevant cranial asymmetry indexes is proposed. Thus, a template database is created and then aligned with the unlabelled patient through an iterative closest point (ICP) strategy. Here, an initial rigid alignment followed by an affine one are applied to remove global misalignments between each template and the patient. Next, a non-rigid alignment is used to deform the template information to the patient-specific shape. The final position of each landmark is computed as a local weight average of all candidate results. From the identified landmarks, a head’s coordinate system is automatically estimated and later used to estimate cranial asymmetry indexes. The proposed framework was evaluated in 15 synthetic infant head’s model. Overall, the results demonstrated the accuracy of the identification strategy, with a mean average distance of 2.8±0.6 mm between the identified landmarks and the ground-truth. Moreover, for the estimation of cranial asymmetry indexes, a performance comparable to the inter-observer variability was achieved., This work was funded by the project NORTE-01-0145-FEDER-024300, supported by Northern Portugal Regional Operational Programme (Norte2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (FEDER). Furthermore, the authors cknowledge Fundação para a Ciência e a Tecnologia (FCT), Portugal, and the European Social Found, European Union, for funding support through the “Programa Operacional Capital Humano” (POCH) in the scope of the PhD grants SFRH/BD/136721/2018 (Bruno Oliveira), SFRH/BD/136670/2018 (Helena R. Torres), and SFRH/BD/131545/2017 (Fernando Veloso).

Although minimally invasive Nuss procedure is frequently performed to correct Pectus Excavatum, the successful aesthetical outcome is not always ensured. Using the computed tomography (CT) data of six patients, high-quality surfaces of the anterior chest wall were generated, alongside with a personalized corrective-bar. Through finite element method (FEM), replicating the surgical procedure, a simulation of the anterior chest wall correction was conducted. The assessment of this methodology was verified by comparing the metrics from the real meshes (3D scanned before and after surgery) and simulated meshes (obtained before and after FEM). Results show a mean difference of 2.85 +/- 5.77 mm on the point of maximum correction between simulated and real outcomes. No statistical differences were found (p = 0.281). High aesthetical similarity was observed concerning simulated and real outcomes. The proposed methodology presents a patient-specific simulation that may be used to plan, predict and improve the surgical outcome of the Nuss procedure. Further studies should continue to improve the presented methodology., This work has been funded by FEDER funds, through the Competitiveness Factors Operational Programme (COMPETE), and by National funds, through the Foundation for Science and Technology (FCT), under the scope of the projects POCI-01-0145-FEDER-007038; NORTE-01-0145-FEDER-000013; and NORTE-01-0145-FEDER-024300, supported by the Northern Portugal Regional Operational Programme (NORTE 2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (FEDER). Joao Gomes-Fonseca was funded by FCT under the Ph.D. grant PD/BDE/113597/2015.

Surgical training for minimal invasive kidney interventions (MIKI) has huge importance within the urology field. Within this topic, simulate MIKI in a patient-specific virtual environment can be used for pre-operative planning using the real patient's anatomy, possibly resulting in a reduction of intra-operative medical complications. However, the validated VR simulators perform the training in a group of standard models and do not allow patient-specific training. For a patient-specific training, the standard simulator would need to be adapted using personalized models, which can be extracted from pre-operative images using segmentation strategies. To date, several methods have already been proposed to accurately segment the kidney in computed tomography (CT) images. However, most of these works focused on kidney segmentation only, neglecting the extraction of its internal compartments. In this work, we propose to adapt a coupled formulation of the B-Spline Explicit Active Surfaces (BEAS) framework to simultaneously segment the kidney and the renal collecting system (CS) from CT images. Moreover, from the difference of both kidney and CS segmentations, one is able to extract the renal parenchyma also. The segmentation process is guided by a new energy functional that combines both gradient and region-based energies. The method was evaluated in 10 kidneys from 5 CT datasets, with different image properties. Overall, the results demonstrate the accuracy of the proposed strategy, with a Dice overlap of 92.5%, 86.9% and 63.5%, and a point-to-surface error around 1.6 mm, 1.9 mm and 4 mm for the kidney, renal parenchyma and CS, respectively., NORTE-01-0145-FEDER0000I3, and NORTE-01-0145-FEDER-024300, supported by Northern Portugal Regional Operational Programme (Norte2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (FEDER), and also been funded by FEDER funds, through Competitiveness Factors Operational Programme (COMPETE), and by national funds, through the FCT-Fundacao para a Ciência e Tecnologia, under the scope of the project POCI-01-0145-FEDER-007038. The authors acknowledge FCT-Fundação para a Ciância e a Tecnologia, Portugal, and the European Social Found, European Union, for funding support through the Programa Operacional Capital Humano (POCH)., info:eu-repo/semantics/publishedVersion