This imaging application is characterized by large variations in data appearance and limited availability of labeled data.
1 code implementation • 31 Jul 2021 • Shaheer U. Saeed, Yunguan Fu, Vasilis Stavrinides, Zachary M. C. Baum, Qianye Yang, Mirabela Rusu, Richard E. Fan, Geoffrey A. Sonn, J. Alison Noble, Dean C. Barratt, Yipeng Hu
Using 6644 clinical ultrasound images from 249 prostate cancer patients, our results for image classification and segmentation tasks show that the proposed IQA method can be adapted using data with as few as respective 19. 7% and 29. 6% expert-reviewed consensus labels and still achieve comparable IQA and task performance, which would otherwise require a training dataset with 100% expert labels.
In this paper, we present an augmentation policy search method with the goal of improving model classification performance.
The main idea of the proposed method is to retain the feature representations of the source model on the target task data, and to leverage them as an additional source of supervisory signals for regularizing the target model learning, thereby improving its performance under limited training samples.
Morphological analysis of longitudinal MR images plays a key role in monitoring disease progression for prostate cancer patients, who are placed under an active surveillance program.
To regularise the anatomical structures between US and MRI during synthesis, we further propose an adversarial structural constraint.
For this case, we assume that there is a high correlation between the ultrasound video and the corresponding narrative speech audio of the sonographer.
Evaluations for 3 standard plane types show that the model provides a useful guidance signal with an accuracy of 88. 8% for goal prediction and 90. 9% for action prediction.
We evaluate our method on the video saliency datasets DHF1K, Hollywood-2 and UCF-Sports, and the image saliency datasets SALICON and MIT300.
Therefore, there is significant interest in learning representations from unlabelled raw data.
In this paper, in contrast, we present a method to automatically discover and localize anatomical landmarks in medical images.
Performance was assessed based on 608 cross-sectional clinical ultrasound RF images of liver tumors (230 and 378 demonstrating respondent and non-respondent cases, respectively).
The feasibility of the approach to produce realistic looking MR images is demonstrated quantitatively and with a qualitative evaluation compared to real fetal MR images.
In this paper, we investigate utero-placental interface (UPI) detection in 2D placental ultrasound images by formulating it as a semantic contour detection problem.
Classical pairwise image registration methods search for a spatial transformation that optimises a numerical measure that indicates how well a pair of moving and fixed images are aligned.
Secondly, we train a simple softmax regression on the feature activations of each CNN layer in order to evaluate the representations independently of transfer learning hyper-parameters.
no code implementations • 9 Jul 2018 • Yipeng Hu, Marc Modat, Eli Gibson, Wenqi Li, Nooshin Ghavami, Ester Bonmati, Guotai Wang, Steven Bandula, Caroline M. Moore, Mark Emberton, Sébastien Ourselin, J. Alison Noble, Dean C. Barratt, Tom Vercauteren
A median target registration error of 3. 6 mm on landmark centroids and a median Dice of 0. 87 on prostate glands are achieved from cross-validation experiments, in which 108 pairs of multimodal images from 76 patients were tested with high-quality anatomical labels.
During training, the registration network simultaneously aims to maximize similarity between anatomical labels that drives image alignment and to minimize an adversarial generator loss that measures divergence between the predicted- and simulated deformation.
Spatially aligning medical images from different modalities remains a challenging task, especially for intraoperative applications that require fast and robust algorithms.
Pixelwise segmentation of the left ventricular (LV) myocardium and the four cardiac chambers in 2-D steady state free precession (SSFP) cine sequences is an essential preprocessing step for a wide range of analyses.
In this paper, we describe how a patient-specific, ultrasound-probe-induced prostate motion model can be directly generated from a single preoperative MR image.
Sonography synthesis has a wide range of applications, including medical procedure simulation, clinical training and multimodality image registration.
We present an automatic method to describe clinically useful information about scanning, and to guide image interpretation in ultrasound (US) videos of the fetal heart.
Feature tracking Cardiac Magnetic Resonance (CMR) has recently emerged as an area of interest for quantification of regional cardiac function from balanced, steady state free precession (SSFP) cine sequences.
In the case of ultrasound, the Nakagami distribution is a general model of the ultrasonic backscattering envelope under various scattering conditions and densities where it can be employed for characterizing image texture, but the subtle intra-heterogeneities within a given mass are difficult to capture via this model as it works at a single spatial scale.