The elusive nature of the blocking effect: 15 failures to replicate

With the discovery of the blocking effect, learning theory took a huge leap forward, because blocking provided a crucial clue that surprise is what drives learning. This in turn stimulated the development of novel association-formation theories of learning. Eventually, the ability to explain blocking became nothing short of a touchstone for the validity of any theory of learning, including propositional and other nonassociative theories. The abundance of publications reporting a blocking effect and the importance attributed to it suggest that it is a robust phenomenon. Yet, in the current article we report 15 failures to observe a blocking effect despite the use of procedures that are highly similar or identical to those used in published studies. Those failures raise doubts regarding the canonical nature of the blocking effect and call for a reevaluation of the central status of blocking in theories of learning. They may also illustrate how publication bias influences our perspective toward the robustness and reliability of seemingly established effects in the psychological literature

DEEPBEAS3D: Deep Learning and B-Spline Explicit Active Surfaces

Deep learning-based automatic segmentation methods have become state-of-the-art. However, they are often not robust enough for direct clinical application, as domain shifts between training and testing data affect their performance. Failure in automatic segmentation can cause sub-optimal results that require correction. To address these problems, we propose a novel 3D extension of an interactive segmentation framework that represents a segmentation from a convolutional neural network (CNN) as a B-spline explicit active surface (BEAS). BEAS ensures segmentations are smooth in 3D space, increasing anatomical plausibility, while allowing the user to precisely edit the 3D surface. We apply this framework to the task of 3D segmentation of the anal sphincter complex (AS) from transperineal ultrasound (TPUS) images, and compare it to the clinical tool used in the pelvic floor disorder clinic (4D View VOCAL, GE Healthcare; Zipf, Austria). Experimental results show that: 1) the proposed framework gives the user explicit control of the surface contour; 2) the perceived workload calculated via the NASA-TLX index was reduced by 30% compared to VOCAL; and 3) it required 7 0% (170 seconds) less user time than VOCAL (p< 0.00001)Comment: 4 pages, 3 figures, 1 table, conferenc

Automatic segmentation method of pelvic floor levator hiatus in ultrasound using a self-normalising neural network

Segmentation of the levator hiatus in ultrasound allows to extract biometrics which are of importance for pelvic floor disorder assessment. In this work, we present a fully automatic method using a convolutional neural network (CNN) to outline the levator hiatus in a 2D image extracted from a 3D ultrasound volume. In particular, our method uses a recently developed scaled exponential linear unit (SELU) as a nonlinear self-normalising activation function, which for the first time has been applied in medical imaging with CNN. SELU has important advantages such as being parameter-free and mini-batch independent, which may help to overcome memory constraints during training. A dataset with 91 images from 35 patients during Valsalva, contraction and rest, all labelled by three operators, is used for training and evaluation in a leave-one-patient-out cross-validation. Results show a median Dice similarity coefficient of 0.90 with an interquartile range of 0.08, with equivalent performance to the three operators (with a Williams' index of 1.03), and outperforming a U-Net architecture without the need for batch normalisation. We conclude that the proposed fully automatic method achieved equivalent accuracy in segmenting the pelvic floor levator hiatus compared to a previous semi-automatic approach

Automatic characterization of neointimal tissue by intravascular optical coherence tomography

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Three-dimensional myocardial strain estimation from volumetric ultrasound: experimental validation in an animal model

Although real-time three-dimensional echocardiography has the potential to allow for more accurate assessment of global and regional ventricular dynamics compared to the more traditional two-dimensional ultrasound examinations, it still requires rigorous testing and validation against other accepted techniques should it breakthrough as a standard examination in routine clinical practice. Very few studies have looked at a validation of regional functional indices in an in-vivo context. The aim of the present study therefore was to validate a previously proposed 3D strain estimation-method based on elastic registration of subsequent volumes on a segmental level in an animal model. Volumetric images were acquired with a GE Vivid7 ultrasound system in five open-chest sheep instrumented with ultrasonic microcrystals. Radial (epsilon(RR)), longitudinal (epsilon(LL)) and circumferential strain (epsilon(CC)) were estimated during four stages: at rest, during esmolol and dobutamine infusion, and during acute ischemia. Moderate correlations for epsilon(LL) (r=0.63; p<0.01) and epsilon(CC) (r=0.60; p=0.01) were obtained, whereas no significant radial correlation was found. These findings are comparable to the performance of the current state-of-the-art commercial 3D speckle tracking methods

The role of the image phase in cardiac strain imaging

International audienceThis paper reviews our most recent contributions in the field of cardiac deformation imaging, which includes a motion estimation framework based on the conservation of the image phase over time and an open pipeline to benchmark algorithms for cardiac strain imaging in 2D and 3D ultrasound. The paper also shows an original evaluation of the proposed motion estimation technique based on the new benchmarking pipeline

Failures to replicate blocking are surprising and informative : reply to Soto

The blocking effect has inspired numerous associative learning theories and is widely cited in the literature. We recently reported a series of 15 experiments that failed to obtain a blocking effect in rodents. Based on those consistent failures, we claimed that there is a lack of insight into the boundary conditions for blocking. In his commentary, Soto (in press) argues that contemporary associative learning theory does provide a specific boundary condition for the occurrence of blocking, namely the use of same- versus different-modality stimuli. Given that in ten of our 15 experiments same-modality stimuli were used, he claims that our failure to observe a blocking effect is unsurprising. We cannot but disagree with that claim, because of theoretical, empirical, and statistical problems with his analysis. We also address two other possible reasons for a lack of blocking that are referred to in Soto's (in press) analysis, related to generalization and salience, and dissect the potential importance of both. While Soto's (in press) analyses raises a number of interesting points, we see more merit in an empirically guided analysis and call for empirical testing of boundary conditions on blocking