Testicular tumours in children: an approach to diagnosis and management with pathologic correlation.

Testicular tumours are rare in children. Painless scrotal mass is the most frequent clinical presentation. Tumoural markers (alpha-fetoprotein, beta-human gonadotropin chorionic) and hormone levels (testosterone) contribute to the diagnosis and management of a testicular mass in boys. Ultrasonography is the best imaging modality to study testicular tumours. A benign tumour is suggested when ultrasonography shows a mainly cystic component, well-defined borders, echogenic rim or normal to increased echogenicity lesion when compared to the healthy testicular parenchyma. Malignant tumour is suspected when ultrasonography shows inhomogeneous, hypoechoic, not well-circumscribed or diffuse infiltration lesion. However, these ultrasonographic findings may overlap. Colour Doppler, power Doppler, elastography and contrast-enhanced ultrasonography are useful complementary methods to characterise the focal testicular lesions. Chest computerised tomography and abdominopelvic magnetic resonance are necessary to establish the extension in case of malignant proved tumours.Benign tumours are more frequent in prepuberal boys and malignant tumours in pubertal boys. Mature teratoma prepubertal-type is the most common histologic type. Testicular sparing surgery is the choice in benign tumours. Radical inguinal orchiectomy is indicated in malignant tumours. Prognostic is excellent.The purpose of our study is to show an approach to the diagnosis and management of the most frequent testicular tumours in children according to clinical manifestations, imaging findings and tumour markers levels based on histologically confirmed tumours in our hospital.

Eighteen years of experience with pediatric nutcracker syndrome: the importance of the conservative approach.

INTRODUCTION: Nutcracker syndrome (NS) defines an entity generated by the compression of the left renal vein resulting in venous hypertension, which transmitted in a retrograde direction may cause hematuria, proteinuria, and varicocele. The literature concerning exclusively pediatric patients is very rare. OBJECTIVE: To report the authors' experience with pediatric NS in the last 18 years. STUDY DESIGN: This is a retrospective review of the patients followed up in the authors' center with diagnosis of NS based on clinical and imaging tests (ultrasound, computed tomography/magnetic resonance imaging, and phlebography). The primary outcome was the success of the conservative approach in the study patients. RESULTS AND DISCUSSION: A total of 21 patients were diagnosed with NS and followed up for a mean period of 52.3 months (37.1-67.5). The mean age at diagnosis was 11.7 years (9.9-13.4). The most frequent symptom of presentation was hematuria in 16 patients (76.2%), being macroscopic in 75% patients and related to physical exercise in 42.9% patients. Other symptoms were left varicocele in 7 patients (33%) and proteinuria in 6 patients (28.6%). Mild to moderate cases received conservative treatment (change of physical activity, postural hygiene), which achieved resolution of symptoms in 16 patients (76.2%). Five cases (23.8%) finally needed a more aggressive approach. Two (9.5%) of them required endovascular procedures (intravascular stent in the renal vein in 1 case and embolization of the spermatic vein in 1 case); in one (4.8%) of the patients, transposition of the left renal vein and kidney autotransplantation were performed, and 2 (9.5%) of the patients with mild cases required surgical correction of the varicocele. CONCLUSIONS: Hematuria, usually macroscopic and related to physical exercise, is the most frequent symptom in pediatric patients with NS. The authors advocate studying the aortomesenteric junction by abdominal ultrasound in patients with varicocele or with intermittent macroscopic hematuria. Diagnosis is based on non-invasive tests; phlebography should be reserved for severe cases that require an interventionist attitude. A long period of conservative treatment is the first approach for pediatric patients with NS.

Virtual M-Mode for Echocardiography: A New Approach for the Segmentation of the Anterior Mitral Leaflet.

Rheumatic heart disease can result from repeated episodes of acute rheumatic fever, which damages the heart valves and reduces their functionality. Early manifestations of heart valve damage are visible in echocardiography in the form of valve thickening, shape changing and mobility reduction. The quantification of these features is important for a precise diagnosis and it is the main motivation for this work. The first step to make this quantification is to accurately identify and track the anterior mitral leaflet throughout the cardiac cycle. An accurate segmentation and tracking with minimum user interaction is still an open problem in literature due to low image quality, speckle noise, signal dropout and nonrigid deformations. In this work, we propose a novel approach for the identification of the anterior mitral valve leaflet in all frames. The method requires a single user-specified point on the posterior wall of the aorta as input, in the first frame. The echocardiography videos are converted into a new image space, the Virtual M-mode, which samples the original echocardiography image over automatically estimated scanning lines. This new image space not only provides the motion pattern of the posterior wall of the aorta, the anterior wall of the aorta and the posterior wall of the left atrium, but also provides the location of the structures in each frame. The location information is then used to initialize the localized active contours, followed by segmenting the anterior mitral leaflet. Results shown that the new image space has robustly identified the anterior mitral valve leaflet, without any failure. The median modified Hausdorff distance error of the proposed method was 2.3 mm, with a recall of 0.94.

A New Method for the Anterior Mitral Leaflet Segmentation in Echocardiography Videos using the Virtual M-mode Space.

Rheumatic heart disease is responsible for the heart valve damage, caused by repeated episodes of rheumatic fever. The disease commonly inflames and scars the mitral valve of the heart, resulting in thicker, less mobile leaflets, with associated decrease in cardiac efficiency. It is important to measure and quantity the early manifestations of this disease, including variations of the thickness, shape and mobility of the leaflets. These manifestations are visible in an echocardiographic screening process. The first step towards the defined objective is to segment the anterior mitral leaflet throughout the cardiac cycle, enabling the future automatic quantification of mentioned clinical parameters. In this work, a new algorithm for the segmentation of the whole region of the anterior mitral leaflet in the virtual M-mode space is proposed. The algorithm requires a single initialization point on the posterior wall of the aorta, in the first frame of the video. A junction point is then computed, showing the location where the two leaflets connect. This junction point helps to automatically redefine the range of virtual M-mode images required to completely segment the region of the anterior mitral leaflet. The segmented anterior mitral leaflet region in the virtual M-mode space is transferred back to regular image space and its shape refined using localized active contours. Results suggest the suitability of the proposed algorithm for the segmentation of anterior mitral leaflet with a median Dice Similarity Coefficient of 0.63, and with median precision and recall of 58% and 73% respectively.

Fully Automatic Finger Extensor Tendon Segmentation in Ultrasound Images of the Metacarpophalangeal Joint.

In this work a fully automatic system to identify the extensor tendon on ultrasound images of the metacarpophalangeal joint is proposed. These images are used to diagnose rheumatic diseases which are one of the main causes of impairment and pain in developed countries. The early diagnosis of these conditions is crucial to a proper treatment and follow-up and so, a system such as the one proposed here, could be useful to automatically extract relevant information from the resulting images. This work is an extension of a previous published work which uses manual annotations of the skin line, metacarpus and phalange to guide the extensor tendon segmentation. By introducing automatic segmentations of all structures, we expect to create a fully automatic system, which is more interesting to the possible end-users. Results show that, despite an expected loss in the performance, it is still possible to correctly identify the extensor tendon with a Confidence of 88% considering a maximum allowed Modified Hausdorff Distance of 0.5mm.

Extracting Thickness Profiles of Anterior Mitral Leaflets in Echocardiography Videos.

Rheumatic heart disease is the serious consequence of repeated episodes of acute rheumatic fever. It is the major cause of heart valve damage resulting in morbidity and mortality. Its early detection is considered vital to control the disease's progression. The key manifestations that are visible in the early stages of this disease are changes in the thickness, shape and mobility of the mitral valve leaflets. Echocardiography based screening is sensitive enough to identify these changes in early stages of the disease. In this work, an automatic approach is proposed to measure, quantify and analyze the thickness of the anterior mitral leaflet, in an echocardiographic video. The shape of the anterior mitral leaflet is simplified via morphological skeletonization and spline modelling to get the central line of the leaflet. To analyze the overall thickness from the tip to its base, the anterior mitral leaflet is divided into four quartiles. In ach quartile the thickness is measured as the length of the line segment resulting from the intersection of the contour with the normal direction of the central point of each quartile. Finally, the thickness is analyzed by measuring the variance per quartile, divided by leaflet position (open, straight and closed). The comparison between the normal and pathological leaflets are also presented, exhibiting statistical significant differences in all quartiles, especially near the tip of the leaflet.

Three-Dimensional Planning Tool for Breast Conserving Surgery: A Technological Review.

Breast cancer is one of the most common malignancies affecting women worldwide. However, despite its incidence trends have increased, the mortality rate has significantly decreased. The primary concern in any cancer treatment is the oncological outcome but, in the case of breast cancer, the surgery aesthetic result has become an important quality indicator for breast cancer patients. In this sense, an adequate surgical planning and prediction tool would empower the patient regarding the treatment decision process, enabling a better communication between the surgeon and the patient and a better understanding of the impact of each surgical option. To develop such tool, it is necessary to create complete 3D model of the breast, integrating both inner and outer breast data. In this review, we thoroughly explore and review the major existing works that address, directly or not, the technical challenges involved in the development of a 3D software planning tool in the field of breast conserving surgery.

A New Active Contours Approach for Finger Extensor Tendon Segmentation in Ultrasound Images Using Prior Knowledge and Phase Symmetry.

This work proposes a new approach for the segmentation of the extensor tendon in ultrasound images of the second metacarpophalangeal joint (MCPJ). The MCPJ is known to be frequently involved in early stages of rheumatic diseases like rheumatoid arthritis. The early detection and follow up of these diseases is important to start and adapt the treatments properly and, in that way, preventing irreversible damage of the joints. This work relies on an active contours framework, preceded by a phase symmetry preprocessing and with prior knowledge energies, to automatically identify the extensor tendon. Active contours methods are widely used in ultrasound images because of their robustness to speckle noise and ability to join unconnected smaller regions into a coherent shape. The tendon is formulated as a line so open ended active contours were used. Phase symmetry highlights the tendon, by setting a proper scale range and angle span. The distance between structures and the tendon slope were also included to enforce the model based on anatomical characteristics. And finally, the concavity measures were used because, given the anatomy of the finger, we know that the tendon line should have less than two concavities. To solve the active contours energy minimization a genetic algorithm approach was used. Several energy metric configurations were compared using the modified Hausdorff distance and results showed that this segmentation is not only possible, but exhibits errors smaller than 0.5 mm with a confidence of 95% with the phase symmetry preprocessing and energies based on the line neighborhood, area ratio, slope, and concavity measurements.

Tracking large Anterior Mitral Leaflet displacements by incorporating optical flow in an active contours framework.

Echocardiography is an important tool to detect early evidence of mitral valve degradation associated with rheumatic heart disease. The segmentation and tracking of the Anterior Mitral Leaflet helps to quantify the morphologic valve anomalies, such as the leaflet thickening, shape and the mobility changes. The tracking of this leaflet throughout the cardiac cycle is still an open challenge in the research community. The widely used active contours segmentation framework fails when faced with large leaflet displacement. In this work, we propose the integration of optical flow in an open-ended active contour framework to address this difficulty. This additional information promotes solutions with contours next to high leaflet displacements, resulting in superior performance. The algorithm was tested on 9 fully annotated real clinical videos, acquired from the parasternal long axis view. The algorithm is compared with our previous work. Results show a clear improvement in situations where the leaflet exhibits large displacement or irregular shapes, with an average error of 4.5 pixels and a standard deviation of 2 pixels.

Tracking of the anterior mitral leaflet in echocardiographic sequences using active contours.

Echocardiography assessment of cardiac valves plays a vital role in the diagnosis of rheumatic heart disease. In the vast majority of cases, the mitral valve gets affected, leading to the thickening of its leaflets that may result in the fusion of their tips. This changes the appearance and reduces the mobility of the leaflets, which also reduce the heart efficiency. Quantifying such parameters provides diagnostic insight. To achieve that, the first step is to identify and then track fast moving leaflets. This work is focused on Anterior Mitral Leaflet (AML) tracking. Open ended active contours are employed in this work by removing its boundary conditions. The external and internal energy of the contour is modified that extend the capture range, improve snake energy and encourages the leftmost end point of the contour to converge on the moving tip of the AML. Results show that contour points are tracked accurately with an average error of 4.9 pixels and a standard deviation of 2.1 pixels in 9 fully annotated normal sequences of real children clinical assessments.

Segmentation of the metacarpus and phalange in musculoskeletal ultrasound images using local active contours.

This work presents a method for the automatic segmentation of metacarpus and phalange bones in ultrasound images of the second metacarpophalangeal joint (MCPJ) using Active Contours. The MCPJ is known to be the one of the first structures to be affected by rheumatic diseases like rheumatoid arthritis. The early detection and follow-up of this disease is important to prevent irreversible damage of the joints, which occurs continuously and faster if no treatment is used. To our knowledge, there is no automatic system to quantify the extension of the lesions resulting from rheumatic activity. The objective of this work is to identify the metacarpus and the phalange bones using local active contours. To our knowledge, there is no well established method for this problem and this technique has not been used yet in these structures. Results proved that the automatic segmentation is possible with an error of 3 pixels for a confidence of 80%.

Using a multi-agent system approach for microaneurysm detection in fundus images.

OBJECTIVE: Microaneurysms represent the first sign of diabetic retinopathy, and their detection is fundamental for the prevention of vision impairment. Despite several research attempts to develop an automated system to detect microaneurysms in fundus images, none has shown the level of performance required for clinical practice. We propose a new approach, based on a multi-agent system model, for microaneurysm segmentation. METHODS AND MATERIALS: A multi-agent based approach, preceded by a preprocessing phase to allow construction of the environment in which agents are situated and interact, is presented. The proposed method is applied to two available online datasets and results are compared to other previously described approaches. RESULTS: Microaneurysm segmentation emerges from agent interaction. The final score of the proposed approach was 0.240 in the Retinopathy Online Challenge. CONCLUSIONS: We achieved competitive results, primarily in detecting microaneurysms close to vessels, compared to more conventional algorithms. Despite these results not being optimum, they are encouraging and reveal that some improvements may be made.

Quality evaluation of digital fundus images through combined measures.

The evaluation of image quality is an important step before an automatic analysis of retinal images. Several conditions can impair the acquisition of a good image, and minimum image quality requirements should be present to ensure that an automatic or semiautomatic system provides an accurate diagnosis. A method to classify fundus images as low or good quality is presented. The method starts with the detection of regions of uneven illumination and evaluates if the segmented noise masks affect a clinically relevant area (around the macula). Afterwards, focus is evaluated through a fuzzy classifier. An input vector is created extracting three focus features. The system was validated in a large dataset (1454 fundus images), obtained from an online database and an eye clinic and compared with the ratings of three observers. The system performance was close to optimal with an area under the receiver operating characteristic curve of 0.9943.