Automatic multi-view pose estimation in focused cardiac ultrasound.

Focused cardiac ultrasound (FoCUS) is a valuable point-of-care method for evaluating cardiovascular structures and function, but its scope is limited by equipment and operator's experience, resulting in primarily qualitative 2D exams. This study presents a novel framework to automatically estimate the 3D spatial relationship between standard FoCUS views. The proposed framework uses a multi-view U-Net-like fully convolutional neural network to regress line-based heatmaps representing the most likely areas of intersection between input images. The lines that best fit the regressed heatmaps are then extracted, and a system of nonlinear equations based on the intersection between view triplets is created and solved to determine the relative 3D pose between all input images. The feasibility and accuracy of the proposed pipeline were validated using a novel realistic in silico FoCUS dataset, demonstrating promising results. Interestingly, as shown in preliminary experiments, the estimation of the 2D images' relative poses enables the application of 3D image analysis methods and paves the way for 3D quantitative assessments in FoCUS examinations.

Portuguese translation, cross-cultural adaptation and reliability of Young Spine Questionnaire.

OBJECTIVE: To translate and perform the cross-cultural adaptation of the Young Spine Questionnaire (YSQ) into Portuguese, and to assess its reliability. METHOD: Translation and cross-cultural adaptations were conducted according to accepted international standards. A preliminary version underwent pilot-testing with 32 children (11-14 years), equally divided by gender and age. Children were asked to rate each question in terms of clarity and comprehensibility, and to provide general feedback regarding the questionnaire. The final version of the questionnaire was approved by a committee consisting of experts from various fields. Test-retest reliability was assessed on 58 children using Cohen's and Fleiss' Kappa. RESULTS: Translation and cross-cultural adaptation of the YSQ only resulted in minor changes and the children rated all questions as "clear and understandable" in the pilot test, without gender or age differences being detected. Test-retest data was collected with a mean interval of 13 days. Reliability scores ranged from 0.56-0.97, equivalent to "moderate" to "almost perfect" agreement. Most questions (84%) had "substantial" or "almost perfect" agreement. CONCLUSION: The translation and cross-cultural adaptation of YSQ into Portuguese was successfully performed. This questionnaire was also shown to be reliable, supporting its future use in research projects.

Comparative Analysis of Current Deep Learning Networks for Breast Lesion Segmentation in Ultrasound Images.

Automatic lesion segmentation in breast ultrasound (BUS) images aids in the diagnosis of breast cancer, the most common type of cancer in women. Accurate lesion segmentation in ultrasound images is a challenging task due to speckle noise, artifacts, shadows, and lesion variability in size and shape. Recently, convolutional neural networks have demonstrated impressive results in medical image segmentation tasks. However, the lack of public benchmarks and a standardized evaluation method hampers the networks' performance comparison. This work presents a benchmark of seven state-of-the-art methods for the automatic breast lesion segmentation task. The methods were evaluated on a multi-center BUS dataset composed of three public datasets. Specifically, the U-Net, Dynamic U-Net, Semantic Segmentation Deep Residual Network with Variational Autoencoder (SegResNetVAE), U-Net Transformers, Residual Feedback Network, Multiscale Dual Attention-Based Network, and Global Guidance Network (GG-Net) architectures were evaluated. The training was performed with a combination of the cross-entropy and Dice loss functions and the overall performance of the networks was assessed using the Dice coefficient, Jaccard index, accuracy, recall, specificity, and precision. Despite all networks having obtained Dice scores superior to 75%, the GG-Net and SegResNetVAE architectures outperform the remaining methods, achieving 82.56% and 81.90%, respectively. Clinical Relevance- The results of this study allowed to prove the potential of deep neural networks to be used in clinical practice for breast lesion segmentation also suggesting the best model choices.

A sensorized needle guide for ultrasound assisted breast biopsy.

One in every eight women will get breast cancer during their lifetime. Therefore, the early diagnosis of the lesions is fundamental to improve the chances of recovery. To find breast cancers, breast screening using techniques such as mammography and ultrasound (US) imaging scans are often used. When a lesion is found, a breast biopsy is performed to extract a tissue sample for analysis. The breast biopsy is usually assisted by an US to help find the lesion and guide the needle to its location. However, the identification of the needle tip in US image is challenging, possibly resulting in puncture failures. In this paper, we intend to study the potential of a sensorized needle guide system that provides information about the needle angle and displacement in respect to the US probe. Laboratory tests were initially conducted to evaluate the accuracy of each sensor in controlled conditions. After, a practical experiment with the US probe, working as a proof of concept, was performed. The angle sensor showed a root mean square error (RMSE) of 0.48 degrees and the displacement sensor showed a RMSE of 0.26mm after being calibrated. For the US probe tests, the displacement sensor shows high errors in the range of 1.19mm to 2.05mm due to mechanical reasons. Overall, the proposed system showed its potential to be used to accurately estimate needle tip localization throughout breast biopsies guided by US, corroborating its potential clinical application. Clinical relevance - Potential for clinical application where precise needle localization in ultrasound image is required.

Ultrasound training simulator using augmented reality glasses: an accuracy and precision assessment study.

Ultrasound (US) imaging despite being safe, cost-effective, and radiation-free, presents poor quality and artifacts, requiring specific medical training in US probe handling and image evaluation. The use of simulators to train physicians has proven its effectiveness, but most of them require specific facilities and hardware. In the last few years, augmented reality has gained relevance to simulate real scenarios which can avoid large setups and broaden medical training to more physicians. This work proposes a new framework for the training of US images acquisition. It consists of a custom-made application that runs on AR glasses (Microsoft HoloLens 2) and interacts with a US simulator application. The AR glasses track the orientation of a QR code mounted on a US probe, communicating its orientation with the US simulator application. This allows the physician to interact with a US probe seeing in real-time the US image in the physician's field of view. The QR code tracking assessment of the AR glasses was conducted by measuring the orientation accuracy and precision when compared with the measures of an electromagnetic tracking device (i.e., NDI Aurora). The proposed solution presented a good performance, rendering the US image in AR glasses with real-time feedback. Moreover, it can track the QR code on the US probe with an accuracy of 0.755°, and a precision of 0.018°. Overall, the proposed framework presents promising results and the use of AR glasses as a tracking device reached a good performance. Clinical Relevance- Simulation is a relevant tool to train physicians, especially in US imaging. AR glasses can broaden the training to less trained physicians by reducing the need for complex setups. This technology allows the implementation of a more natural user interface, which can be relevant in scenarios where good coordination between the eyes and hands of the physician is necessary (i.e., Biopsies).

Deep learning methods for lesion detection on mammography images: a comparative analysis.

Automatic lesion segmentation in mammography images assists in the diagnosis of breast cancer, which is the most common type of cancer especially among women. The robust segmentation of mammography images has been considered a backbreaking task due to: i) the low contrast of the lesion boundaries; ii) the extremely variable lesions' sizes and shapes; and iii) some extremely small lesions on the mammogram image. To overcome these drawbacks, Deep Learning methods have been implemented and have shown impressive results when applied to medical image segmentation. This work presents a benchmark for breast lesion segmentation in mammography images, where six state-of-the-art methods were evaluated on 1692 mammograms from a public dataset (CBIS-DDSM), and compared considering the following six metrics: i) Dice coefficient; ii) Jaccard index; iii) accuracy; iv) recall; v) specificity; and vi) precision. The base U-Net, UNETR, DynUNet, SegResNetVAE, RF-Net, MDA-Net architectures were trained with a combination of the cross-entropy and Dice loss functions. Although the networks presented Dice scores superior to 86%, two of them managed to distinguish themselves. In general, the results demonstrate the efficiency of the MDA-Net and DynUnet with Dice scores of 90.25% and 89.67%, and accuracy of 93.48% and 93.03%, respectively. Clinical Relevance--- The presented comparative study allowed to identify the current performance of deep learning strategies on the segmentation of breast lesions.

A deep learning method for kidney segmentation in 2D ultrasound images.

Ultrasound (US) is a medical imaging modality widely used for diagnosis, monitoring, and guidance of surgical procedures. However, the accurate interpretation of US images is a challenging task. Recently, portable 2D US devices enhanced with Artificial intelligence (AI) methods to identify, in real-time, specific organs are widely spreading worldwide. Nevertheless, the number of available methods that effectively work in such devices is still limited. In this work, we evaluate the performance of the U-NET architecture to segment the kidney in 2D US images. To accomplish this task, we studied the possibility of using multiple sliced images extracted from 3D US volumes to achieve a large, variable, and multi-view dataset of 2D images. The proposed methodology was tested with a dataset of 66 3D US volumes, divided in 51 for training, 5 for validation, and 10 for testing. From the volumes, 3792 2D sliced images were extracted. Two experiments were conducted, namely: (i) using the entire database (WWKD); and (ii) using images where the kidney area is > 500 mm2 (500KD). As a proof-of-concept, the potential of our strategy was tested in real 2D images (acquired with 2D probes). An average error of 2.88 ± 2.63 mm in the testing dataset was registered. Moreover, satisfactory results were obtained in our initial proof-of-concept using pure 2D images. In short, the proposed method proved, in this preliminary study, its potential interest for clinical practice. Further studies are required to evaluate the real performance of the proposed methodology. Clinical Relevance- In this work a deep learning methodology to segment the kidney in 2D US images is presented. It may be a relevant feature to be included in the recent portable US ecosystems easing the interpretation of image and consequently the clinical analysis.

Kidney Segmentation in 3-D Ultrasound Images Using a Fast Phase-Based Approach.

Renal ultrasound (US) 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 3-D US images represents a relevant and challenging task in clinical practice. In this article, a novel framework is proposed to accurately segment the kidney in 3-D US 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- 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 data sets: 1) 15 3-D challenging poor-quality US images used for experimental development, parameters assessment, and evaluation and 2) 42 3-D US 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.

Technical Note: Assessment of electromagnetic tracking systems in a surgical environment using ultrasonography and ureteroscopy instruments for percutaneous renal access.

PURPOSE: Electromagnetic tracking systems (EMTSs) have been proposed to assist the percutaneous renal access (PRA) during minimally invasive interventions to the renal system. However, the influence of other surgical instruments widely used during PRA (like ureteroscopy and ultrasound equipment) in the EMTS performance is not completely known. This work performs this assessment for two EMTSs [Aurora® Planar Field Generator (PFG); Aurora® Tabletop Field Generator (TTFG)]. METHODS: An assessment platform, composed by a scaffold with specific supports to attach the surgical instruments and a plate phantom with multiple levels to precisely translate or rotate the surgical instruments, was developed. The median accuracy and precision in terms of position and orientation were estimated for the PFG and TTFG in a surgical environment using this platform. Then, the influence of different surgical instruments (alone or together), namely analogic flexible ureterorenoscope (AUR), digital flexible ureterorenoscope (DUR), two-dimensional (2D) ultrasound (US) probe, and four-dimensional (4D) mechanical US probe, was assessed for both EMTSs by coupling the instruments to 5-DOF and 6-DOF sensors. RESULTS: Overall, the median positional and orientation accuracies in the surgical environment were 0.85 mm and 0.42° for PFG, and 0.72 mm and 0.39° for TTFG, while precisions were 0.10 mm and 0.03° for PFG, and 0.20 mm and 0.12° for TTFG, respectively. No significant differences were found for accuracy between EMTSs. However, PFG showed a tendency for higher precision than TTFG. AUR, DUR, and 2D US probe did not influence the accuracy and precision of both EMTSs. In opposition, the 4D probe distorted the signal near the attached sensor, making readings unreliable. CONCLUSIONS: Ureteroscopy- and ultrasonography-assisted PRA based on EMTS guidance are feasible with the tested AUR or DUR together with the 2D probe. More studies must be performed to evaluate the probes and ureterorenoscopes' influence before their use in PRA based on EMTS guidance.

Should we use papilla morphology to estimate the size of the terminal common bile duct during endoscopic retrograde cholangiopancreatography?

OBJECTIVE: A small common bile duct (CBD) diameter has been associated with complications and with a difficult biliary cannulation. Previous studies suggested that this diameter can be predicted during the endoscopic retrograde cholangiopancreatography (ERCP) simply by observing the papillary morphology. Despite this published suggestion there is no study addressing this topic. This study evaluated a possible association between the morphology of the major papilla and the diameter of the terminal CBD (t-CBD). METHODS: Observational cross-sectional study including consecutive patients with naïve papillae was referred for ERCP in two affiliated university hospitals. The transverse (p-transv) and longitudinal measures (p-long) of the papilla were obtained using a visual method. Papillae were classified into nonprominent, prominent, bulging or other. The t-CBD was measured 1 cm from the papilla using fluoroscopic images in prone/supine. Measurements were performed by two senior endoscopists and outcomes were evaluated using correlation and linear regression model. RESULTS: We included 245 patients with a median age of 76 years. The median p-transv for each type of papillae was as follows: nonprominent = 6 mm, prominent = 9 mm, bulging = 15 mm and other = 6 mm; P < 0.001. The median t-CBD for nonprominent = 7.62 mm, prominent = 8.34 mm, bulging = 8.60 mm and other = 8.52 mm; P = 0.40. The correlation between the transverse and longitudinal measures of papilla and the t-CBD were 0.0092 and 0.0614, respectively. In the regression model, the t-CBD diameter was not explained by papilla's size or morphology (R = 1.70%; P = 0.80). CONCLUSION: The morphology of the papilla must not be used as a predictor of the diameter of the CBD as there is no correlation between these two items.

Surface-based registration between CT and US for image-guided percutaneous renal access - A feasibility study.

PURPOSE: As a crucial step in accessing the kidney in several minimally invasive interventions, percutaneous renal access (PRA) practicality and safety may be improved through the fusion of computed tomography (CT) and ultrasound (US) data. This work aims to assess the potential of a surface-based registration technique and establish an optimal US acquisition protocol to fuse two-dimensional (2D) US and CT data for image-guided PRA. METHODS: Ten porcine kidney phantoms with fiducial markers were imaged using CT and three-dimensional (3D) US. Both images were manually segmented and aligned. In a virtual environment, 2D contours were extracted by slicing the 3D US kidney surfaces and using usual PRA US-guided views, while the 3D CT kidney surfaces were transformed to simulate positional variability. Surface-based registration was performed using two methods of the iterative closest point algorithm (point-to-point, ICP1; and point-to-plane, ICP2), while four acquisition variants were studied: (a) use of single-plane (transverse, SPT ; or longitudinal, SPL ) vs bi-plane views (BP); (b) use of different kidney's coverage ranges acquired by a probe's sweep; (c) influence of sweep movements; and (d) influence of the spacing between consecutive slices acquired for a specific coverage range. RESULTS: BP view showed the best performance (TRE = 2.26 mm) when ICP2 method, a wide kidney coverage range (20°, with slices spaced by 5°), and a large sweep along the central longitudinal view were used, showing a statistically similar performance (P = 0.097) to a full 3D US surface registration (TRE = 2.28 mm). CONCLUSIONS: An optimal 2D US acquisition protocol was evaluated. Surface-based registration, using multiple slices and specific sweep movements and views, is here suggested as a valid strategy for intraoperative image fusion using CT and US data, having the potential to be applied to other image modalities and/or interventions.

A novel multi-atlas strategy with dense deformation field reconstruction for abdominal and thoracic multi-organ segmentation from computed tomography.

Anatomical evaluation of multiple abdominal and thoracic organs is generally performed with computed tomography images. Owing to the large field-of-view of these images, automatic segmentation strategies are typically required, facilitating the clinical evaluation. Multi-atlas segmentation (MAS) strategies have been widely used with this process, requiring multiple alignments between the target image and the set of known datasets, and subsequently fusing the alignment results to obtain the final segmentation. Nonetheless, current MAS strategies apply a global alignment of a deformable object, per organ, subdividing the segmentation process into multiple ones and losing the spatial information among nearby organs. This paper presents a novel MAS approach. First, a coarse-to-fine method with multiple global alignments (one per organ) is used. To make the method spatially coherent, these individual organs' global transformations are then fused in one using a dense deformation field reconstruction strategy. Second, from the candidate segmentations obtained, the final segmentation is estimated through an organ-based label fusion approach. The proposed method is evaluated and compared against a conventional MAS strategy through the segmentation of twelve abdominal and thoracic organs from the VISCERAL Anatomy benchmark. Average Dice coefficients for the liver, spleen, lungs and kidneys are all higher than 90%, are around 85% for the aorta, trachea and sternum and 70% for the pancreas, urinary bladder and gallbladder. The novel MAS strategy, with dense deformation field reconstruction, shows competitive results against other state-of-the-art methods, proving its added value for the segmentation of abdominal and thoracic organs, mainly for highly variable organs.

A new methodology for assessment of pectus excavatum correction after bar removal in Nuss procedure: Preliminary study.

PURPOSE: The objective is to present a new methodology to assess quantitatively the impact of bar removal on the anterior chest wall, among patients with pectus excavatum who have undergone the Nuss procedure, and present a preliminary study using this methodology. METHODS: We propose to acquire, for each patient, the surface of the anterior chest wall using a three-dimensional laser scanner at subsequent time points (short term: before and after surgery; long term: follow-up visit, 6months, and 12months after surgery). After surfaces postprocessing, the changes are assessed by overlapping and measuring the distances between surfaces. In this preliminary study, three time points were acquired and two assessments were performed: before vs after bar removal (early) and before vs 2-8weeks after bar removal (interim). In 21 patients, the signed distances and volumes between surfaces were computed and the data analysis was performed. RESULTS: This methodology revealed useful for monitoring changes in the anterior chest wall. On average, the mean, maximum, and volume variations, in the early assessment, were -0.1±0.1cm, -0.6±0.2cm, and 47.8±22.2cm3, respectively; and, in the interim assessment, were -0.5±0.2cm, -1.3±0.4cm, and 122.1±47.3cm3, respectively (p<0.05). Data analysis revealed that the time the bar was in situ was inversely and significantly correlated with postretraction and was a relevant predictor of its decrease following surgery (p<0.05). Additionally, gender and age suggested influencing the outcome. CONCLUSIONS: This methodology is novel, objective and safe, helping on follow-up of pectus excavatum patients. Moreover, the preliminary study suggests that the time the bar was in situ may be the main determinant of the anterior chest wall retraction following bar removal. Further studies should continue to corroborate and reinforce the preliminary findings, by increasing the sample size and performing long-term assessments. LEVELS OF EVIDENCE: III.