Validation of percutaneous puncture trajectory during renal access using 4D ultrasound reconstruction

"Progress in Biomedical Optics and Imaging, vol. 16, nr. 43"Background: An accurate percutaneous puncture is essential for disintegration and removal of renal stones. Although this procedure has proven to be safe, some organs surrounding the renal target might be accidentally perforated. This work describes a new intraoperative framework where tracked surgical tools are superimposed within 4D ultrasound imaging for security assessment of the percutaneous puncture trajectory (PPT). Methods: A PPT is first generated from the skin puncture site towards an anatomical target, using the information retrieved by electromagnetic motion tracking sensors coupled to surgical tools. Then, 2D ultrasound images acquired with a tracked probe are used to reconstruct a 4D ultrasound around the PPT under GPU processing. Volume hole-filling was performed in different processing time intervals by a tri-linear interpolation method. At spaced time intervals, the volume of the anatomical structures was segmented to ascertain if any vital structure is in between PPT and might compromise the surgical success. To enhance the volume visualization of the reconstructed structures, different render transfer functions were used. Results: Real-time US volume reconstruction and rendering with more than 25 frames/s was only possible when rendering only three orthogonal slice views. When using the whole reconstructed volume one achieved 8-15 frames/s. 3 frames/s were reached when one introduce the segmentation and detection if some structure intersected the PPT. Conclusions: The proposed framework creates a virtual and intuitive platform that can be used to identify and validate a PPT to safely and accurately perform the puncture in percutaneous nephrolithotomy.The authors acknowledge to Foundation for Science and Technology (FCT) - Portugal for the fellowships with references: SFRH/BD/74276/2010.info:eu-repo/semantics/publishedVersio

Thoracic wall reconstruction using ultrasound images to model/bend the thoracic prosthesis for correction of pectus excavatum

Pectus excavatum is the most common congenital deformity of the anterior thoracic wall. The surgical correction of such deformity, using Nuss procedure, consists in the placement of a personalized convex prosthesis into sub-sternal position to correct the deformity. The aim of this work is the CT-scan substitution by ultrasound imaging for the pre-operative diagnosis and pre-modeling of the prosthesis, in order to avoid patient radiation exposure. To accomplish this, ultrasound images are acquired along an axial plane, followed by a rigid registration method to obtain the spatial transformation between subsequent images. These images are overlapped to reconstruct an axial plane equivalent to a CT-slice. A phantom was used to conduct preliminary experiments and the achieved results were compared with the corresponding CT-data, showing that the proposed methodology can be capable to create a valid approximation of the anterior thoracic wall, which can be used to model/bend the prosthesis.Fundação para a Ciencia e Tecnologia (FCT

Characterization of the workspace and limits of operation of laser treatments for vascular lesions of the lower limbs

The increase of the aging population brings numerous challenges to health and aesthetic segments. Here, the use of laser therapy for dermatology is expected to increase since it allows for non-invasive and infection-free treatments. However, existing laser devices require doctors’ manually handling and visually inspecting the skin. As such, the treatment outcome is dependent on the user’s expertise, which frequently results in ineffective treatments and side effects. This study aims to determine the workspace and limits of operation of laser treatments for vascular lesions of the lower limbs. The results of this study can be used to develop a robotic-guided technology to help address the aforementioned problems. Specifically, workspace and limits of operation were studied in eight vascular laser treatments. For it, an electromagnetic tracking system was used to collect the real-time positioning of the laser during the treatments. The computed average workspace length, height, and width were 0.84 ± 0.15, 0.41 ± 0.06, and 0.78 ± 0.16 m, respectively. This corresponds to an average volume of treatment of 0.277 ± 0.093 m3. The average treatment time was 23.2 ± 10.2 min, with an average laser orientation of 40.6 ± 5.6 degrees. Additionally, the average velocities of 0.124 ± 0.103 m/s and 31.5 + 25.4 deg/s were measured. This knowledge characterizes the vascular laser treatment workspace and limits of operation, which may ease the understanding for future robotic system development.The authors acknowledge Fundação para a Ciência e a Tecnologia (FCT), Portugal and the European Social Fund, European Union, for funding support through [the “Programa Operacional Capital Humano” (POCH) in the scope of the PhD], grants number [SFRH/BD/136721/2018 (B. Oliveira) and SFRH/BD/136670/2018 (H. Torres)]. Moreover, authors gratefully acknowledge the funding of the projects “NORTE-01-0145-FEDER-000045” and “NORTE-01-0145-FEDER-000059”, supported by [Northern Portugal Regional Operational Programme (NORTE 2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (FEDER)]. It was also funded by [national funds, through the FCT and FCT/MCTES in the scope of the projects UIDB/05549/2020, UIDP/05549/2020, and LASI-LA/P/0104/2020]

A-scan ultrasound system for real-time puncture safety assessment during percutaneous nephrolithotomy

Background: Kidney stone is a major universal health problem, affecting 10% of the population worldwide. Percutaneous nephrolithotomy is a first-line and established procedure for disintegration and removal of renal stones. Its surgical success depends on the precise needle puncture of renal calyces, which remains the most challenging task for surgeons. This work describes and tests a new ultrasound based system to alert the surgeon when undesirable anatomical structures are in between the puncture path defined through a tracked needle. Methods: Two circular ultrasound transducers were built with a single 3.3-MHz piezoelectric ceramic PZT SN8, 25.4 mm of radius and resin-epoxy matching and backing layers. One matching layer was designed with a concave curvature to work as an acoustic lens with long focusing. The A-scan signals were filtered and processed to automatically detect reflected echoes. Results: The transducers were mapped in water tank and tested in a study involving 45 phantoms. Each phantom mimics different needle insertion trajectories with a percutaneous path length between 80 and 150 mm. Results showed that the beam cross-sectional area oscillates around the ceramics radius and it was possible to automatically detect echo signals in phantoms with length higher than 80 mm. Conclusions: This new solution may alert the surgeon about anatomical tissues changes during needle insertion, which may decrease the need of X-Ray radiation exposure and ultrasound image evaluation during percutaneous puncture.The authors acknowledge to Foundation for Science and Technology (FCT) - Portugal for the fellowships with references: SFRH/BD/74276/2010 and the Brazilian agencies CAPES and CNPq. The present submission corresponds to original research work of the authors and has never been submitted elsewhere for publication.info:eu-repo/semantics/publishedVersio

Augmented synthetic dataset with structured light to develop Ai-based methods for breast depth estimation

Breast interventions are common healthcare procedures that nor mally require experienced professionals, expensive setups, and high execution times. With the evolution of robot-assisted technologies and image analysis algorithms, new methodologies can be imple mented to facilitate the interventions in this area. To enable the introduction of robot-assisted approaches for breast procedures, strategies with real-time capacity and high precision for 3D breast shape estimation are required. In this paper, it is proposed to fuse the structured light (SL) and deep learning (DL) techniques to perform the depth estimation of the breast shape with high precision. First, multiple synthetic datasets of breasts with different printed patterns, resembling the SL technique, are created. Thus, it is possi ble to take advantage of the pattern’s deformation induced by the breast surface in order to improve the quality of the depth infor mation and to study the most suitable design. Then, distinct DL architectures, taken from the literature, were implemented to esti mate the breast shape from the created datasets and study the DL architectures’ influence on depth estimation. The results obtained with the introduction of a yellow grid pattern, composed of thin stripes, fused with the DenseNet-161 architecture achieved the best results. Overall, the current study demonstrated the potential of the proposed practice for breast depth estimation or other human body parts in the future when we rely exclusively on 2D images.The authors acknowledge the funding of the projects "NORTE01-0145-FEDER000045” and "NORTE-01-0145-FEDER-000059", supported by Northern Portugal Regional Operational Programme (NORTE 2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (FEDER). It was also funded by national funds, through the Fundação para a Ciência e a Tecnologia (FCT) and FCT/MCTES in the scope of the projects UIDB/05549/2020,UIDP/05549/2020 and LASILA/P/0104/2020. The authors also acknowledge 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 (B. Oliveira) and SFRH/BD/136670/2018 (H. Torres)

Dual consistency loss for contour-aware segmentation in medical images

Medical image segmentation is a paramount task for several clinical applications, namely for the diagnosis of pathologies, for treatment planning, and for aiding image-guided surgeries. With the development of deep learning, Convolutional Neural Networks (CNN) have become the state-of-the-art for medical image segmentation. However, issues are still raised concerning the precise object boundary delineation, since traditional CNNs can produce non-smooth segmentations with boundary discontinuities. In this work, a U-shaped CNN architecture is proposed to generate both pixel-wise segmentation and probabilistic contour maps of the object to segment, in order to generate reliable segmentations at the object's boundaries. Moreover, since the segmentation and contour maps must be inherently related to each other, a dual consistency loss that relates the two outputs of the network is proposed. Thus, the network is enforced to consistently learn the segmentation and contour delineation tasks during the training. The proposed method was applied and validated on a public dataset of cardiac 3D ultrasound images of the left ventricle. The results obtained showed the good performance of the method and its applicability for the cardiac dataset, showing its potential to be used in clinical practice for medical image segmentation.Clinical Relevance-The proposed network with dual consistency loss scheme can improve the performance of state-of-the-art CNNs for medical image segmentation, proving its value to be applied for computer-aided diagnosis.- (undefined

A region-based algorithm for automatic bone segmentation in volumetric CT

In Computed Tomography (CT), bone segmentation is considered an important step to extract bone parameters, which are frequently useful for computer-aided diagnosis, surgery and treatment of many diseases such as osteoporosis. Consequently, the development of accurate and reliable segmentation techniques is essential, since it often provides a great impact on quantitative image analysis and diagnosis outcome. This chapter presents an automated multistep approach for bone segmentation in volumetric CT datasets. It starts with a three-dimensional (3D) watershed operation on an image gradient magnitude. The outcome of the watershed algorithm is an over-partioning image of many 3D regions that can be merged, yielding a meaningful image partitioning. In order to reduce the number of regions, a merging procedure was performed that merges neighbouring regions presenting a mean intensity distribution difference of ±15%. Finally, once all bones have been distinguished in high contrast, the final 3D bone segmentation was achieved by selecting all regions with bone fragments, using the information retrieved by a threshold mask. The bones contours were accurately defined according to the watershed regions outlines instead of considering the thresholding segmentation result. This new method was tested to segment the rib cage on 185 CT images, acquired at the São João Hospital of Porto (Portugal) and evaluated using the dice similarity coefficient as a statistical validation metric, leading to a coefficient mean score of 0.89. This could represent a step forward towards accurate and automatic quantitative analysis in clinical environments and decreasing time-consumption, user dependence and subjectivity.The authors acknowledge to Foundation for Science and Technology (FCT) - Portugal for the fellowships with the references: SFRH/BD/74276/2010; SFRH/BD/68270/2010; and, SFRH/BPD/46851/2008. This work was also supported by FCT R&D project PTDC/SAU-BEB/103368/2008

Classification of chronic venous disorders using an ensemble optimization of convolutional neural networks

Chronic Venous Disorders (CVD) of lower limbs are one of the most prevalent medical conditions, affecting 35% of adults in Europe and North America. The early diagnosis of CVD is critical, however, the diagnosis relies on a visual recognition of the various venous disorders which is time- consuming and dependent on the physician's expertise. Thus, automatic strategies for the classification of the CVD severity are claimed. This paper proposed an automatic ensemble-based strategy of Deep Convolutional Neural Networks (DCNN) for the classification of CVDs severity from medical images. First, a clinical dataset containing 1376 images of patients' legs with CVD of 5 different levels of severity was constructed. Then, the constructed dataset was randomly split into training, testing, and validation datasets. Subsequently, a set of DCNN were individually applied to the images for classification. Finally, instead of a traditional voting ensemble strategy, extracted feature vectors from each DCNN were concatenated and fed into a new ensemble optimization network. Experiments showed that the proposed strategy achieved a classification with 93.8%, 93.4%, 92.4% of accuracy, precision, and recall, respectively. Moreover, compared to the traditional ensemble strategy, improvement in the accuracy of ~2% was registered. The proposed strategy showed to be accurate and robust for the diagnosis of CVD severity from medical images. Nevertheless, further research using an extensive clinical database is required to validate the potential of this strategy.The authors acknowledge 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 (B. Oliveira) and SFRH/BD/136670/2018 (H. Torres). Moreover, authors gratefully acknowledge the funding of the projects "NORTE-01-0145-FEDER000045” and "NORTE-01-0145-FEDER-000059", supported by Northern Portugal Regional Operational Programme (NORTE 2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (FEDER). It was also funded by national funds, through the FCT and FCT/MCTES in the scope of the project LASI-LA/P/0104/2020, UIDB/00319/2020, UIDB/05549/2020 and UIDP/05549/2020

Automatic modeling of an orthotic bracing for nonoperative correction of Pectus Carinatum

Pectus Carinatum is a deformity of the chest wall, characterized by an anterior protrusion of the sternum, often corrected surgically due to cosmetic motivation. This work presents an alternative approach to the current open surgery option, proposing a novel technique based on a personalized orthosis. Two different processes for the orthosis' personalization are presented. One based on a 3D laser scan of the patient chest, followed by the reconstruction of the thoracic wall mesh using a radial basis function, and a second one, based on a computer tomography scan followed by a neighbouring cells algorithm. The axial position where the orthosis is to be located is automatically calculated using a Ray-Triangle intersection method, whose outcome is input to a pseudo Kochenek interpolating spline method to define the orthosis curvature. Results show that no significant differences exist between the patient chest physiognomy and the curvature angle and size of the orthosis, allowing a better cosmetic outcome and less initial discomfort.The authors acknowledge to Foundation for Science and Technology (FCT) - Portugal for the fellowships with the references: SFRH/BD/74276/2010; SFRH/BD/68270/2010; UMINHO/BI/95/2012; and, SFRH/BPD/46851/2008. This work was also supported by FCT R&D project PTDC/SAUBEB/103368/2008

Personalized dynamic phantom of the right and left ventricles based on patient-specific anatomy for echocardiography studies — Preliminary results

Dynamic phantoms of the heart are becoming a reality, with their use spread across both medical and research fields. Their purpose is to mimic the cardiac anatomy, as well as its motion. This work aims to create a dynamic, ultrasound-compatible, realistic and flexible phantom of the left and right ventricles, with application in the diagnosis, planning, treatment and training in the cardiovascular field for studies using echocardiography. Here, we focus on its design and production with polyvinyl alcohol cryogel (PVA-C), to be assembled with a pump and an electromechanical (E/M) system in a water tank. Based on a patient-specific anatomical model and produced using a 3D printing technique and molding, the PVA-C phantom mimics the ventricles' natural anatomy and material properties, while the pump and E/M systems mimic the natural movements and pressures. The PVA-C phantom was assessed by imaging and measuring it using a four-dimensional ultrasound machine. The PVA-C phantom demonstrated to be a versatile option to produce patient-specific biventricular models, preserving their shape after manufacturing and presenting good echogenic properties. Both chambers were clearly seen in the ultrasound images, together with the interventricular septum and the myocardial wall. Automated left ventricle measures revealed a decrease of its volume with regard to the designed model (98 ml to 74 ml). Overall, the preliminary results are satisfactory and encourage its use for the abovementioned purposesFEDER funds through the Competitiveness Factors Operational Programme (COMPETE), and by National funds through the Foundation for Science and Technology (FCT) under the project POCI -01-0145-FEDER-007038 and EXPL/BBB-BMD/2473/2013, and by the projects NORTE-01-0145-FEDER-000013 and NORTE-01-0145-FEDER-024300, supported by the NORTE 2020, under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (FEDER). J. Gomes-Fonseca, P. Morais, S. Queirós, and F. Veloso were funded by FCT under the Ph.D. grants PD/BDE/113597/2015, SFRH/BD/95438/2013, SFRH/BD/93443/2013, and SFRH/BD/131545/2017info:eu-repo/semantics/publishedVersio