Automatic Frame Selection Using MLP Neural Network in Ultrasound Elastography

Ultrasound elastography estimates the mechanical properties of the tissue from two Radio-Frequency (RF) frames collected before and after tissue deformation due to an external or internal force. This work focuses on strain imaging in quasi-static elastography, where the tissue undergoes slow deformations and strain images are estimated as a surrogate for elasticity modulus. The quality of the strain image depends heavily on the underlying deformation, and even the best strain estimation algorithms cannot estimate a good strain image if the underlying deformation is not suitable. Herein, we introduce a new method for tracking the RF frames and selecting automatically the best possible pair. We achieve this by decomposing the axial displacement image into a linear combination of principal components (which are calculated offline) multiplied by their corresponding weights. We then use the calculated weights as the input feature vector to a multi-layer perceptron (MLP) classifier. The output is a binary decision, either 1 which refers to good frames, or 0 which refers to bad frames. Our MLP model is trained on in-vivo dataset and tested on different datasets of both in-vivo and phantom data. Results show that by using our technique, we would be able to achieve higher quality strain images compared to the traditional methods of picking up pairs that are 1, 2 or 3 frames apart. The training phase of our algorithm is computationally expensive and takes few hours, but it is only done once. The testing phase chooses the optimal pair of frames in only 1.9 ms

Registration of ultrasound volumes based on Euclidean distance transform

During neurosurgical operations, surgeons can decide to acquire intraoperative data to better proceed with the removal of a tumor. A valid option is given by ultrasound (US) imaging, which can be easily obtained at subsequent surgical stages, giving therefore multiple updates of the resection cavity. To improve the efficacy of the intraoperative guidance, neurosurgeons may benefit from having a direct correspondence between anatomical structures identified at different US acquisitions. In this context, the commonly available neuronavigation systems already provide registration methods, which however are not enough accurate to overcome the anatomical changes happening during resection. Therefore, our aim with this work is to improve the registration of intraoperative US volumes. In the proposed methodology, first a distance mapping of automatically segmented anatomical structures is computed and then the transformed images are utilized in the registration step. Our solution is tested on a public dataset of 17 cases, where the average landmark registration error between volumes acquired at the beginning and at the end of neurosurgical procedures is reduced from 3.55mm to 1.27mm

Improving 3D ultrasound prostate localisation in radiotherapy through increased automation of interfraction matching.

Background and purpose Daily image guidance is standard care for prostate radiotherapy. Innovations which improve the accuracy and efficiency of ultrasound guidance are needed, particularly with respect to reducing interobserver variation. This study explores automation tools for this purpose, demonstrated on the Elekta Clarity Autoscan®. The study was conducted as part of the Clarity-Pro trial (NCT02388308). Materials and methods Ultrasound scan volumes were collected from 32 patients. Prostate matches were performed using two proposed workflows and the results compared with Clarity's proprietary software. Gold standard matches derived from manually localised landmarks provided a reference. The two workflows incorporated a custom 3D image registration algorithm, which was benchmarked against a third-party application (Elastix). Results Significant reductions in match errors were reported from both workflows compared to standard protocol. Median (IQR) absolute errors in the left-right, anteroposterior and craniocaudal axes were lowest for the Manually Initiated workflow: 0.7(1.0) mm, 0.7(0.9) mm, 0.6(0.9) mm compared to 1.0(1.7) mm, 0.9(1.4) mm, 0.9(1.2) mm for Clarity. Median interobserver variation was ≪0.01 mm in all axes for both workflows compared to 2.2 mm, 1.7 mm, 1.5 mm for Clarity in left-right, anteroposterior and craniocaudal axes. Mean matching times was also reduced to 43 s from 152 s for Clarity. Inexperienced users of the proposed workflows attained better match precision than experienced users on Clarity. Conclusion Automated image registration with effective input and verification steps should increase the efficacy of interfraction ultrasound guidance compared to the current commercially available tools

Global Ultrasound Elastography Using Convolutional Neural Network

Displacement estimation is very important in ultrasound elastography and failing to estimate displacement correctly results in failure in generating strain images. As conventional ultrasound elastography techniques suffer from decorrelation noise, they are prone to fail in estimating displacement between echo signals obtained during tissue distortions. This study proposes a novel elastography technique which addresses the decorrelation in estimating displacement field. We call our method GLUENet (GLobal Ultrasound Elastography Network) which uses deep Convolutional Neural Network (CNN) to get a coarse time-delay estimation between two ultrasound images. This displacement is later used for formulating a nonlinear cost function which incorporates similarity of RF data intensity and prior information of estimated displacement. By optimizing this cost function, we calculate the finer displacement by exploiting all the information of all the samples of RF data simultaneously. The Contrast to Noise Ratio (CNR) and Signal to Noise Ratio (SNR) of the strain images from our technique is very much close to that of strain images from GLUE. While most elastography algorithms are sensitive to parameter tuning, our robust algorithm is substantially less sensitive to parameter tuning.Comment: 4 pages, 4 figures; added acknowledgment section, submission type late

A Feature-Driven Active Framework for Ultrasound-Based Brain Shift Compensation

A reliable Ultrasound (US)-to-US registration method to compensate for brain shift would substantially improve Image-Guided Neurological Surgery. Developing such a registration method is very challenging, due to factors such as missing correspondence in images, the complexity of brain pathology and the demand for fast computation. We propose a novel feature-driven active framework. Here, landmarks and their displacement are first estimated from a pair of US images using corresponding local image features. Subsequently, a Gaussian Process (GP) model is used to interpolate a dense deformation field from the sparse landmarks. Kernels of the GP are estimated by using variograms and a discrete grid search method. If necessary, the user can actively add new landmarks based on the image context and visualization of the uncertainty measure provided by the GP to further improve the result. We retrospectively demonstrate our registration framework as a robust and accurate brain shift compensation solution on clinical data acquired during neurosurgery

Deformation Aware Augmented Reality for Craniotomy using 3D/2D Non-rigid Registration of Cortical Vessels

International audienceIntra-operative brain shift is a well-known phenomenon that describes non-rigid deformation of brain tissues due to gravity and loss of cerebrospinal fluid among other phenomena. This has a negative influence on surgical outcome that is often based on pre-operative planning where the brain shift is not considered. We present a novel brain-shift aware Augmented Reality method to align pre-operative 3D data onto the deformed brain surface viewed through a surgical microscope. We formulate our non-rigid registration as a Shape-from-Template problem. A pre-operative 3D wire-like deformable model is registered onto a single 2D image of the cortical vessels, which is automatically segmented. This 3D/2D registration drives the underlying brain structures, such as tumors, and compensates for the brain shift in sub-cortical regions. We evaluated our approach on simulated and real data composed of 6 patients. It achieved good quantitative and qualitative results making it suitable for neurosurgical guidance

Common Limitations of Image Processing Metrics:A Picture Story

While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation. Performance metrics are particularly key for meaningful, objective, and transparent performance assessment and validation of the used automatic algorithms, but relatively little attention has been given to the practical pitfalls when using specific metrics for a given image analysis task. These are typically related to (1) the disregard of inherent metric properties, such as the behaviour in the presence of class imbalance or small target structures, (2) the disregard of inherent data set properties, such as the non-independence of the test cases, and (3) the disregard of the actual biomedical domain interest that the metrics should reflect. This living dynamically document has the purpose to illustrate important limitations of performance metrics commonly applied in the field of image analysis. In this context, it focuses on biomedical image analysis problems that can be phrased as image-level classification, semantic segmentation, instance segmentation, or object detection task. The current version is based on a Delphi process on metrics conducted by an international consortium of image analysis experts from more than 60 institutions worldwide.Comment: This is a dynamic paper on limitations of commonly used metrics. The current version discusses metrics for image-level classification, semantic segmentation, object detection and instance segmentation. For missing use cases, comments or questions, please contact [email protected] or [email protected]. Substantial contributions to this document will be acknowledged with a co-authorshi

O método "Neubauer" aplicado ao estudo do potássio nos solos do estado de São Paulo

Para determinar as quantidades de elementos minerais do solo, disponíveis às plantas, pode-se lançar mão de métodos biológicos, dentre os quais se destaca o de Neubauer e Schneider. Êste método tem sido bastante empregado para solos de clima temperado. Pouco se sabe, porém, de seus resultados para os solos tropicais e subtropicais. A fim de avaliar a sua eficácia, para os nossos solos, efetuou-se uma série de experiências, cujos resultados constituem o objeto do presente trabalho. O método Neubauer foi aplicado para determinação do potássio, tendo-se utilizado o centeio e o arroz, em amostras dos quatro principais tipos de solos do Estado de São Paulo, quais sejam : Arenito Bauru, Massapé-salmourão, Terra roxa legítima e Solo humoso de baixada. Os resultados, obtidos foram comparados com os teores trocáveis de potássio, obtidos por análise química direta. Utilizando o centeio, verificou-se que a extração do potássio trocável, pela planta, variou de 83 a 114% no Arenito Bauru, de 30% a 81% no Massapé-salmourão, de 22 a 38% na Terra roxa legítima e de 52 a 84% no Solo humoso de baixada. Nos casos em que se empregou o arroz, as extrações variaram de 68 a 107% no Arenito Bauru, de 52 a 147% no Massapé-salmourão, de 4 a 76% na Terra roxa legítima e de 72 a 122% no Solo humoso de baixada. Os valores superiores a 100% significam que a planta extraiu parte do potássio, numa forma mais fixa que a "trocável". Os dados mostram que o arroz, de modo geral, apresenta uma capacidade de extração do potássio maior que o centeio. Dentre os 35 resultados médios obtidos com o centeio, em apenas 2 dêles houve uma absorção superior a 24 mg de K2O, limite êsse considerado por Neubauer e Schneider como indicador de um solo suficientemente provido de potássio. De conformidade com êsse método, 33 dos 35 resultados obtidos correspondem a solos que necessitariam de adubação potássica, fato êsse que não está de acôrdo com o que a experimentação de campo tem concluído para as diferentes culturas no Estado de São Paulo.<br>In order to determine the quantities of rnineral elements in soils that are available to plants, biological methods of measurements such as that described by Neubauer and Schneider, have been used. This particular method has been widely employed for study of soils of temperate climates. However, very little was known as to the application and value of this biological method for study of tropical and subtropical soils. The purpose of the experiments described in this paper was evaluate the efficiency of the Neubauer method, using rye and rice plants, for determining available potassium in samples of the four principal soil types of the State of São Paulo. The soil types studied were Arenito Bauru, Massapé-salmourão, Terra roxa legítima and a Humus soil. The Arenito Bauru soil is a sand soil, with 50 - 60% of sand (> 0,2 mm and < 2 mm) and 2 - 10% of clay (< 0,002 mm). The Massapé-salmourão soil is a soil type with 30 - 50% of sand, 20 - 30% of clay and it generally has micas as primary mineral. The Terra roxa legítima soil is a soil which contains 10 - 20% of sand, 35 - 40% of clay and it is originated from basalts. The Humus soil presents a variable composition with a high content of organic matter. The results obtained from the biological tests were compared with the amounts of exchangeable potassium determined by chemical analysis. When rye was used as a test plant it was found that the relative amount of potassium extracted by the plants varied from 83 to 114% in Arenito Bauru, 30 to 81% in Massapé salmourão, 22 to 84% in Humus soil. In the tests where rice plants were used the amounts of available potassium extracted varied from 68 to 107% in Arenito Bauru, 52 to 147% in Massapé salmourão, 4 to 76% in Terra roxa legítima and from 72 to 122% in Humus soil. The values greater than 100% signify that the test plant extracted a portion of the fixed potassium not exchangeable. These data show that the rice plants in general had a greater capacity for extraction of potassium than did rye plants. Within the 35 average results obtained from measurements with rye plants only 2 were found to have absorbed more than 24 mg of K2O. This amount of potassium was considered by Neubauer and Schneider, to indicate that the soil is provided with sufficient potassium. The results from the tests using the biological method then indicate that in 33 out of 35 cases studied the soils lacked potassium. This, however, is contrary to results obtained from field experiments on these soils