A novel ultrasound image segmentation based on a dynamical statistical shape model

Ultrasound (US) imaging is considered as one of the most advanced diagnostic tools in medical use. However, a drawback of the medical US imaging is its poor quality of the image, which is affected by speckle noise. In this paper, a MultiLayer Back-Propagation Neural Networks (MLBPNNs) with the Epanechnikov fuzzy function is proposed to reduce the speckle, and while at the same time, enhance the lesion boundaries of the US image. The main goal of the proposed method is to improve the quality of US image so as to improve the quality of the humans interpretation and the computer systems autoedge detection. In order to automatically detect the lesion boundary by a computer system, an edge enhancement is required. Evaluating the simulation results by Peak Signal to Noise Ratio (PSNR), Normalized Mean Square Error (NMSE), Detail Variance (DV), and Background Variance (BV), the proposed method demonstrates an increased performance of reducing the speckle and enhancing the edge. The proposed method has higher PSNR than conventional methods and can remove the speckle sufficiently, so that tumor boundaries of real US breast tumor image could be preserved and detected.

The visual quality of ultrasound (US) images is crucial for clinical diagnosis and treatment. The main source of image quality degradation is the inherent speckle noise generated during US image acquisition. Current deep learning-based methods cannot preserve the maximum boundary contrast when removing noise and speckle. In this paper, we address the issue by proposing a novel wavelet-based generative adversarial network (GAN) for real-time high-quality US image reconstruction, viz. WGAN-DUS. First, we propose a batch normalization module (BNM) to balance the importance of each sub-band image and fuse sub-band features simultaneously. Then, a wavelet reconstruction module (WRM) integrated with a cascade of wavelet residual channel attention block (WRCAB) is proposed to extract distinctive sub-band features used to reconstruct denoised images. A gradual tuning strategy is proposed to fine-tune our generator for better despeckling performance. We further propose a wavelet-based discriminator and a comprehensive loss function to effectively suppress speckle noise and preserve the image features. Besides, we have designed an algorithm to estimate the noise levels during despeckling of real US images. The performance of our network was then evaluated on natural, synthetic, simulated and clinical US images and compared against various despeckling methods. To verify the feasibility of WGAN-DUS, we further extend our work to uterine fibroid segmentation with the denoised US image of the proposed approach. Experimental result demonstrates that our proposed method is feasible and can be generalized to clinical applications for despeckling of US images in real-time without losing its fine details.

For computer-assisted percutaneous scaphoid fixation, a patient-specific bone model is required for surgical planning. This bone model is commonly derived from pre- operative computed tomography (CT) or magnetic resonance imaging (MRI) data. We propose an approach for bone model derivation based on intra-operative 3D ultrasound (US) imaging for the cases, where pre-operative diagnostic CT or MRI are not indicated or available. As scaphoid bone surfaces are only partially visible in sonographic images, we employ the Transformer-based AdaPoinTr architecture to incorporate statistical morphological knowledge for the completion of partial bone surfaces extracted from sonographic images. For the generation of datasets, we built a statistical shape model (SSM) based on 85 scaphoid bone models. From this SSM, we generated 12288 full scaphoid models for training. 20 additional scaphoid bone models were used for testing. Partial models for both training and testing were generated by subsampling the full models, mimicking 3D US imaging from a volar probe position. Evaluation of the final trained model on the test subset showed a mean symmetric distance of 0.3 mm between original and completed scaphoid models, with an inference time of 0.2 s per model. We furthermore planned screws based on the completed test models and evaluated their fit for the original models. We found no screw protrusion for any tested model, with a mean safety margin to bone surface of 0.7 mm. This study shows feasibility of our approach for US-based bone model generation; future work may aim at integrating dorsal surface information.

Purpose: Ultrasound (US) is sensitive to many cancers that are not well detected by mammography in dense breasts, and characterizes those cysts requiring no further diagnosis. But US screening sees many apparent abnormalities, increasing callbacks, and misses many cancers detected by their microcalcifications. If sufficient US image quality and coverage can be achieved in this geometry, a combined US/Breast Tomosynthesis (US/BT) system should allow rapid identification of structures of interest in corresponding modalities, reducing misidentification errors in diagnosis and allowing reduction of diagnostic follow up of probably benign masses in screening. In screening, adding high quality ultrasound registered to BT should increase detection of cancers in dense breasts as with manual scanning.Photoacoustic tomography (PAT) is sensitive to vascular anomalies including negligible flow. The penetration appears to be adequate for PAT imaging from both sides of the compressed breast. The ability of such optical absorption imaging at different wavelengths (spectroscopic PAT, S‐ PAT) to distinguish oxy‐ and deoxyhemoglobin appears to add to discrimination of benign from malignant masses and might find use in selecting and tracking therapies. Coregistered BT, US and optical imaging might well provide similar screening and diagnostic effectiveness in a single, 15 minute examination, as the combination of current mammography and contrast MRI examinations.Methods: The combined US/BT system has been described earlier. A pilot study was performed of BT and AUS in 51 cases going to biopsy, with the mass visible in BT and in the volume covered by AUS.US images from a new Breast Light and Ultrasound Combined Imaging (BLUCI) system are registered to the US images from the US/BT system for initial studies. BLUCI allows limited angle, transmission speed of sound (SOS) imaging with the same type of 570 element, broadband array as is used for PAT. Our PAT is performed with an 8 cm diameter PFDF polymer transducer array. This added imaging mode might provide similar information as elasticity imaging, but with fewer artifacts.Results: In the preliminary study, addition of automated US did not change the average accuracy, Az, over BT in this small number of cases where only masses visible in BT were analyzed. Each reader, on a case by case question, responded very positively to ‐ If screening were performed by BT (and US) in this fashion, would tomosynthesis alone allow you to avoid categorizing this case as BIRADS 0 and instead enable you to categorize it to avoid additional imaging?The challenge of pulse echo imaging through two layers of lobes (on either side of the midline) with single or two view automated scanning appears to be met in the, currently, separate ultrasound/S‐PAT scanner on a mammographic frame using compound scanning from both sides of the breast (Carson, et al. at this meeting). Initial success in achieving rapid, full acoustic coupling in humans, and in performing PAT and SOS imaging of breast phantoms and specimens will be presented. PAT revealed simulated vessels to 5 cm depth. SOS shows a cylindrical object with the expected resolution of limited angle tomography.Conclusion: Essentially full, high resolution pulse echo coverage of the breast and large volume PAT (∼ an 8 cm diameter cylinder) appear possible in reasonable exam times. The promise of these particular 3 modalities in the mammographic geometry and the level of our current systems justifies initiation of an enhanced screening study of US/BT, with SOS and/or PAT added as method development and exam times allow.

Noninvasive ultrasound (US) brain therapies, including US brain stimulation, US drug delivery by BBB opening, or US thrombolysis, have great potential in clinical applications. But target positioning is a challenge. Dual-mode US system provides a promising solution, which can carry out US therapy function under the guidance of US imaging. However, the brain skull usually deteriorates the brain image seriously, because of the great amplitude of US echoes reflected from the skull. In order to improve the guidance function of US image, we investigated the relationship of skull structures and US image in rodents. Computerized tomography (CT) images of the heads of four rats were scanned by a micro-CT system. US skull imaging was carried out by a custom-built US imaging and stimulation system with a dual-mode 2D matrix array with 1 MHz center frequency and 256 elements. The features of the CT images and the US images of rat skulls were carefully recognized, which were used to identify the coordinates of the rat brain from US images. After the US imaging and brain position was determined, multi-target BBB opening was carried out based on the identified coordinate. The result indicated that the deviation of the coordinate was less than 1 mm. US skull imaging will be a useful method for image-guided noninvasive therapy applications.

Ultrasound (US) imaging is a widely used clinical diagnostic tool in medical imaging techniques. It is a comparatively safe, economical, painless, portable, and noninvasive real-time tool compared to the other imaging modalities. However, the image quality of US imaging is severely affected by the presence of speckle noise and blur during the acquisition process. In order to ensure a high-quality clinical diagnosis, US images must be restored by reducing their speckle noise and blur. In general, speckle noise is modeled as a multiplicative noise following a Rayleigh distribution and blur as a Gaussian function. Hereto, we propose an intelligent estimator based on artificial neural networks (ANNs) to estimate the variances of noise and blur, which, in turn, are used to obtain an image without discernible distortions. A set of statistical features computed from the image and its complex wavelet sub-bands are used as input to the ANN. In the proposed method, we solve the inverse Rayleigh function numerically for speckle reduction and use the Richardson-Lucy algorithm for de-blurring. The performance of this method is compared with that of the traditional methods by applying them to a synthetic, physical phantom and clinical data, which confirms better restoration results by the proposed method.

In this paper, region-difference filters for the segmentation of liver ultrasound (US) images are proposed. Region-difference filters evaluate maximum difference of the average of two regions of the window around the center pixel. Implementing the filters on the whole image gives region-difference image. This image is then converted into binary image and morphologically operated for segmenting the desired lesion from the ultrasound image. The proposed method is compared with the maximum a posteriori-Markov random field (MAP-MRF), Chan-Vese active contour method (CV-ACM), and active contour region-scalable fitting energy (RSFE) methods. MATLAB code available online for the RSFE method is used for comparison whereas MAP-MRF and CV-ACM methods are coded in MATLAB by authors. Since no comparison is available on common database for the performance of the three methods, therefore, performance comparison of the three methods and proposed method was done on liver US images obtained from PGIMER, Chandigarh, India and from online resource. A radiologist blindly analyzed segmentation results of the 4 methods implemented on 56 images and had selected the segmentation result obtained from the proposed method as best for 46 test US images. For the remaining 10 US images, the proposed method performance was very near to the other three segmentation methods. The proposed segmentation method obtained the overall accuracy of 99.32% in comparison to the overall accuracy of 85.9, 98.71, and 68.21% obtained by MAP-MRF, CV-ACM, and RSFE methods, respectively. Computational time taken by the proposed method is 5.05s compared to the time of 26.44, 24.82, and 28.36s taken by MAP-MRF, CV-ACM, and RSFE methods, respectively.

The core of our novel method and system consists of augmenting the standard features of an ultrasound probe by adding functional information from real-time hemodynamic flow computation. It is well known that the quality of ultrasound (US) imaging is very much operator dependent. Our hypothesis is that combining real-time Navier-Stokes simulation with US imaging may reveal inconsistency in mass conservation along the vascular structure that shows when the US acquisition needs to be redone. Augmenting a light wireless US imaging device with robust flow simulation may reveal itself to be a valuable tool to improve the quality of diagnostic with shear stress indicators. In this paper, we describe the main concept of our cyber-physical system to augment an US probe. However robust simulation of Navier-Stokes flow in real time remains a challenging problem. The focus of this paper is on the description and efficiency results of a new parallel domain decomposition algorithm to deliver that level of performance.

This paper presents a computerized segmentation method for breast lesions on ultrasound (US) images. It consists of first applying a contrast-enhanced approach, i.e., a contrast-limited adaptive histogram equalization. Then, aiming at removing speckle and enhancing the lesion boundary, an anisotropic diffusion filter, guided by texture descriptors derived from a set of Gabor filters, is applied. To eliminate the distant pixels that do not belong to the tumor, the resulting filtered image is multiplied by a constraint Gaussian function. By doing so, both the segmentation and the marker functions are generated and could be used in the marker-controlled watershed transformation algorithm to create potential lesion boundaries. Finally, to determine the lesion contour, the average radial derivative function is evaluated. The proposed method was tested with 50 breast US images and 60 simulated "ultrasound-like" images. Accuracy and precision of the segmentation method were then assessed. For the accuracy, three parameters were used: Overlap ratio (OR), normalized residual value (nrv), and proportional distance (PD) between contours. The average results for US images were OR = 0.86 +/- 0.05, nrv = 0.16 +/- 0.06, and PD = 6.58 +/- 2.52%. For simulated ultrasound-like images, a better performance (OR = 0.92 +/- 0.01, nrv = 0.08 +/- 0.01, and PD = 3.20 +/- 0.53%) was achieved. The segmentation method proposed was capable of delineating the lesion contours with high accuracy in comparison to both the radiologists' delineations and the true delineations of simulated images. Moreover, this method was also found to be robust to human-dependent parameters variations.

Objective: To analyze the diagnostic, monitoring, and procedural applications of ultrasound (US) imaging in neurocritical care (NCC) patients.Method: US imaging has been extensively validated in various subset of critically ill patients, but not specifically in the NCC population. We reviewed the clinical applications of US imaging for heart, vascular, brain, and lung evaluation and for possible procedural uses in NCC patients. Major neurosurgical books, journals, testimonials, authors’ personal experience, and scientific databases were analyzed.Results: Cardiac US imaging provides accurate information at NCC arrival to stratify risk factors, including presence of atrial septal defect/patent formen ovale, abnormal ventricular function, or pericardial effusion, and to monitor cardiac anatomy and function during the NCC stay for guiding goal-directed therapy. Vascular US in NCC patients has three especially relevant indications: to screen anatomy and flow in extracranial supra-aortic arteries, to diagnose deep vein thrombosis, and to optimize the safety of central venous catheterization. Brain US has important clinical applications in the NCC, including transcranial Doppler and emerging techniques for cerebral blood flow evaluation with contrast-enhanced US imaging. Lung US, as demonstrated in other intensive care unit patients, provides accurate diagnosis of anatomical and functional abnormalities and enables diagnosis of pleural effusion, pneumothorax, lung consolidation, pulmonary abscess and interstitial-alveolar syndrome, and lung recruitment/derecruitment. US imaging can effectively guide percutaneous tracheostomy.Conclusion: In conclusion, US imaging is an important diagnostic tool that provides real-time information at the bedside to stratify risk, monitor for complications, and guide invasive procedures in NCC patients.

Photoacoustic tomography (PAT) offers structural and functional imaging of living biological tissue with highly sensitive optical absorption contrast and excellent spatial resolution comparable to medical ultrasound (US) imaging. We report the development of a fully integrated PAT and US dual-modality imaging system, which performs signal scanning, image reconstruction and display for both photoacoustic (PA) and US imaging all in a truly real-time manner. The backprojection (BP) algorithm for PA image reconstruction is optimized to reduce the computational cost and facilitate parallel computation on a state of the art graphics processing unit (GPU) card. For the first time, PAT and US imaging of the same object can be conducted simultaneously and continuously, at a real time frame rate, presently limited by the laser repetition rate of 10 Hz. Noninvasive PAT and US imaging of human peripheral joints in vivo were achieved, demonstrating the satisfactory image quality realized with this system. Another experiment, simultaneous PAT and US imaging of contrast agent flowing through an artificial vessel was conducted to verify the performance of this system for imaging fast biological events. The GPU based image reconstruction software code for this dual-modality system is open source and available for download from http://sourceforge.net/projects/pat realtime .

The aim of the present study was to determine the validity of ultrasound (US) imaging versus magnetic resonance imaging (MRI) for measuring anterior thigh muscle, subcutaneous adipose tissue (SAT), and fascia thickness. Twenty healthy, moderately active participants (aged 49.1 ± 9.74 (36–64) years), underwent imaging of the anterior thigh, using ultrasound and MRI modalities on the same day. Images were analyzed offline to assess the level of agreement between US and MRI measurements. Pearson’s correlation coefficient showed an excellent relationship between US imaging and MRI for measuring muscle (r = 0.99, p < 0.01), SAT (r = 0.99, p < 0.01), and non-contractile tissue (SAT combined with perimuscular fascia) thickness (r = 0.99, p < 0.01). Perimuscular fascia thickness measurement showed a poor correlation between modalities (r = 0.39, p < 0.01). Intra-class correlation coefficients (ICC3,1) also showed excellent correlation of the measurements with ICC = 0.99 for muscle thickness, SAT, and non-contractile tissue, but not for perimuscular fascia, which showed poor agreement ICC = 0.36. Bland and Altman plots demonstrated excellent agreement between US imaging and MRI measurements. Criterion validity was demonstrated for US imaging against MRI, for measuring thickness of muscle and SAT, but not perimuscular fascia alone on the anterior thigh. The US imaging technique is therefore applicable for research and clinical purposes for muscle and SAT.

Identifying the proper orientation of the pelvis is a critical step in accurate placement of the femur prosthesis in the acetabulum in Total Hip Replacement \n(THR) surgeries. The general approach to localize the orientation of the pelvis coordinate system is to use X-ray fluoroscopy to guide the procedure. An \nalternative can be employing intra-operative ultrasound (US) imaging with pre-operative CT scan or fluoroscopy imaging. In this paper, we propose to replace \nthe need of pre-operative imaging by using a statistical shape model of the pelvis, constructed from several CT images. We then propose an automatic deformable \nintensity-based registration of the anatomical atlas to a sparse set of 2D ultrasound images of the pelvis in order to localize its anatomical coordinate system. \nIn this registration technique, we first extract a set of 2D slices from a single instance of the pelvic atlas. Each individual 2D slice is generated based on \nthe location of a corresponding 2D ultrasound image. Next, we create simulated ultrasound images out of the 2D atlas slices and calculate a similarity metric \nbetween the simulated images and the actual ultrasound images. The similarity metric guides an optimizer to generate an instance of the atlas that best matches \nthe ultrasound data. We demonstrated the feasibility of our proposed approach on two male human cadaver data. The registration was able to localize a \npatient-specific pelvic coordinate system with origin translation error of 2 mm and 3.45 mm, and average axes rotation error of 3.5 degrees and 3.9 degrees for \nthe two cadavers, respectively.\n\nCopyright 2010 Society of Photo-Optical Instrumentation Engineers. \nOne print or electronic copy may be made for personal use only. Systematic reproduction and distribution, \nduplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.

The quality of ultrasound (US) images for the obstetric examination is crucial for accurate biometric measurement. However, manual quality control is a labor intensive process and often impractical in a clinical setting. To improve the efficiency of examination and alleviate the measurement error caused by improper US scanning operation and slice selection, a computerized fetal US image quality assessment (FUIQA) scheme is proposed to assist the implementation of US image quality control in the clinical obstetric examination. The proposed FUIQA is realized with two deep convolutional neural network models, which are denoted as L-CNN and C-CNN, respectively. The L-CNN aims to find the region of interest (ROI) of the fetal abdominal region in the US image. Based on the ROI found by the L-CNN, the C-CNN evaluates the image quality by assessing the goodness of depiction for the key structures of stomach bubble and umbilical vein. To further boost the performance of the L-CNN, we augment the input sources of the neural network with the local phase features along with the original US data. It will be shown that the heterogeneous input sources will help to improve the performance of the L-CNN. The performance of the proposed FUIQA is compared with the subjective image quality evaluation results from three medical doctors. With comprehensive experiments, it will be illustrated that the computerized assessment with our FUIQA scheme can be comparable to the subjective ratings from medical doctors.

In order to utilize both ultrasound (US) and computed tomography (CT) images of the liver concurrently for medical applications such as diagnosis and image-guided intervention, non-rigid registration between these two types of images is an essential step, as local deformation between US and CT images exists due to the different respiratory phases involved and due to the probe pressure that occurs in US imaging. This paper introduces a voxel-based non-rigid registration algorithm between the 3D B-mode US and CT images of the liver. In the proposed algorithm, to improve the registration accuracy, we utilize the surface information of the liver and gallbladder in addition to the information of the vessels inside the liver. For an effective correlation between US and CT images, we treat those anatomical regions separately according to their characteristics in US and CT images. Based on a novel objective function using a 3D joint histogram of the intensity and gradient information, vessel-based non-rigid registration is followed by surface-based non-rigid registration in sequence, which improves the registration accuracy. The proposed algorithm is tested for ten clinical datasets and quantitative evaluations are conducted. Experimental results show that the registration error between anatomical features of US and CT images is less than 2 mm on average, even with local deformation due to different respiratory phases and probe pressure. In addition, the lesion registration error is less than 3 mm on average with a maximum of 4.5 mm that is considered acceptable for clinical applications.