Searching for a tactile target: the impact of set-size on the N140cc

The time needed to find a visual target amongst distractors (search task) can increase as a function of the distractors’ number (set-size) in the search-array (inefficient search). While the allocation of attention in search tasks has been extensively investigated and debated in the visual domain, little is known about these mechanisms in touch. Initial behavioral evidence shows inefficient search behavior when participants have to distinguish between target and distractors defined by their vibro-tactile frequencies. In the present study, to investigate the allocation of attention to items of the search-array we measured the N140cc during a tactile task in which the set-size was manipulated. The N140cc is a lateralized component of event-related brain potentials recently described as a psychophysiological marker of attentional allocation in tactile search tasks. Participants localized the target, a singleton frequency, while ignoring one, three or five homogeneous distractors. Results showed that error rates linearly increased as a function of set-size, while response times were not affected. Reliable N140cc components were observed for all set-sizes. Crucially, the N140cc amplitude decreased as the number of distractors increased. We argue that the presence of additional distractors hindered the preattentive analysis of the search array resulting in increased uncertainty about the target location (inefficient preattentive stage). This, in turn, increased the variability of the deployment of attention to the target, resulting in reduced N140cc amplitudes. Consistent with existing behavioral evidence, these findings highlight systematic differences between the visual and the tactile attentional systems

Distance Estimation of an Unknown Person from a Portrait

We propose the first automated method for estimating distance from frontal pictures of unknown faces. Camera calibration is not necessary, nor is the reconstruction of a 3D representation of the shape of the head. Our method is based on estimating automatically the position of face and head landmarks in the image, and then using a regressor to estimate distance from such measurements. We collected and annotated a dataset of frontal portraits of 53 individuals spanning a number of attributes (sex, age, race, hair), each photographed from seven distances. We find that our proposed method outperforms humans performing the same task. We observe that different physiognomies will bias systematically the estimate of distance, i.e. some people look closer than others. We expire which landmarks are more important for this task

Peripheral neurological disturbances, autonomic dysfunction, and antineuronal antibodies in adult celiac disease before and after a gluten-free diet

Thirty-two consecutive adult celiac disease (CD) patients (pts), complaining of peripheral neuropathy (12 pts), autonomic dysfunction (17 pts), or both (3 pts), were evaluated to assess the presence of neurological damage (by clinical neurological evaluation and electrophysiological study) and antineuronal antibodies and to assess the effect of a gluten-free diet (GFD) on the course of the neurological symptoms and on antineuronal antibodies. At entry, 12 of 32 (38%) pts showed signs and symptoms of neurological damage: 7 of 12 (58%), peripheral neurological damage; 3 of 12 (25%), autonomic dysfunction; and 2 (17%), both peripheral neurological damage and autonomic dysfunction. The overall TNS score was 105 at entry. Anti-GM1 antibodies were present in 5 of 12 (42%) pts: 3 showed peripheral neurological damage and 2 showed both peripheral neurological damage and autonomic dysfunction. One year after the GFD was started, histological lesions were still present in only 10 of 12 (83%) pts. TNS score was 99, 98, 98, and 101 at the 3rd, 6th, 9th, and 12th month after the GFD was started, so it did not improve throughout the follow-up. None of the pts showed disappearance of antineuronal antibodies throughout the follow-up. We conclude that adult CD patients may show neurological damage and presence of antineuronal antibodies. Unfortunately, these findings do not disappear with a GFD

Strategising template-guided needle placement for MR-targeted prostate biopsy

Clinically significant prostate cancer has a better chance to be sampled during ultrasound-guided biopsy procedures, if suspected lesions found in pre-operative magnetic resonance (MR) images are used as targets. However, the diagnostic accuracy of the biopsy procedure is limited by the operator-dependent skills and experience in sampling the targets, a sequential decision making process that involves navigating an ultrasound probe and placing a series of sampling needles for potentially multiple targets. This work aims to learn a reinforcement learning (RL) policy that optimises the actions of continuous positioning of 2D ultrasound views and biopsy needles with respect to a guiding template, such that the MR targets can be sampled efficiently and sufficiently. We first formulate the task as a Markov decision process (MDP) and construct an environment that allows the targeting actions to be performed virtually for individual patients, based on their anatomy and lesions derived from MR images. A patient-specific policy can thus be optimised, before each biopsy procedure, by rewarding positive sampling in the MDP environment. Experiment results from fifty four prostate cancer patients show that the proposed RL-learned policies obtained a mean hit rate of 93% and an average cancer core length of 11 mm, which compared favourably to two alternative baseline strategies designed by humans, without hand-engineered rewards that directly maximise these clinically relevant metrics. Perhaps more interestingly, it is found that the RL agents learned strategies that were adaptive to the lesion size, where spread of the needles was prioritised for smaller lesions. Such a strategy has not been previously reported or commonly adopted in clinical practice, but led to an overall superior targeting performance when compared with intuitively designed strategies.Comment: Paper submitted and accepted to CaPTion (Cancer Prevention through early detecTion) @ MICCAI 2022 Worksho

Domain generalization for prostate segmentation in transrectal ultrasound images: A multi-center study

Prostate biopsy and image-guided treatment procedures are often performed under the guidance of ultrasound fused with magnetic resonance images (MRI). Accurate image fusion relies on accurate segmentation of the prostate on ultrasound images. Yet, the reduced signal-to-noise ratio and artifacts (e.g., speckle and shadowing) in ultrasound images limit the performance of automated prostate segmentation techniques and generalizing these methods to new image domains is inherently difficult. In this study, we address these challenges by introducing a novel 2.5D deep neural network for prostate segmentation on ultrasound images. Our approach addresses the limitations of transfer learning and finetuning methods (i.e., drop in performance on the original training data when the model weights are updated) by combining a supervised domain adaptation technique and a knowledge distillation loss. The knowledge distillation loss allows the preservation of previously learned knowledge and reduces the performance drop after model finetuning on new datasets. Furthermore, our approach relies on an attention module that considers model feature positioning information to improve the segmentation accuracy. We trained our model on 764 subjects from one institution and finetuned our model using only ten subjects from subsequent institutions. We analyzed the performance of our method on three large datasets encompassing 2067 subjects from three different institutions. Our method achieved an average Dice Similarity Coefficient (Dice) of 94.0±0.03 and Hausdorff Distance (HD95) of 2.28 mm in an independent set of subjects from the first institution. Moreover, our model generalized well in the studies from the other two institutions (Dice: 91.0±0.03; HD95: 3.7 mm and Dice: 82.0±0.03; HD95: 7.1 mm). We introduced an approach that successfully segmented the prostate on ultrasound images in a multi-center study, suggesting its clinical potential to facilitate the accurate fusion of ultrasound and MRI images to drive biopsy and image-guided treatments

Domain generalization for prostate segmentation in transrectal ultrasound images: A multi-center study

Prostate biopsy and image-guided treatment procedures are often performed under the guidance of ultrasound fused with magnetic resonance images (MRI). Accurate image fusion relies on accurate segmentation of the prostate on ultrasound images. Yet, the reduced signal-to-noise ratio and artifacts (e.g., speckle and shadowing) in ultrasound images limit the performance of automated prostate segmentation techniques and generalizing these methods to new image domains is inherently difficult. In this study, we address these challenges by introducing a novel 2.5D deep neural network for prostate segmentation on ultrasound images. Our approach addresses the limitations of transfer learning and finetuning methods (i.e., drop in performance on the original training data when the model weights are updated) by combining a supervised domain adaptation technique and a knowledge distillation loss. The knowledge distillation loss allows the preservation of previously learned knowledge and reduces the performance drop after model finetuning on new datasets. Furthermore, our approach relies on an attention module that considers model feature positioning information to improve the segmentation accuracy. We trained our model on 764 subjects from one institution and finetuned our model using only ten subjects from subsequent institutions. We analyzed the performance of our method on three large datasets encompassing 2067 subjects from three different institutions. Our method achieved an average Dice Similarity Coefficient (Dice) of 94.0±0.03 and Hausdorff Distance (HD95) of 2.28 mm in an independent set of subjects from the first institution. Moreover, our model generalized well in the studies from the other two institutions (Dice: 91.0±0.03; HD95: 3.7 mm and Dice: 82.0±0.03; HD95: 7.1 mm). We introduced an approach that successfully segmented the prostate on ultrasound images in a multi-center study, suggesting its clinical potential to facilitate the accurate fusion of ultrasound and MRI images to drive biopsy and image-guided treatments

Clinicopathological features of the rare form of Creutzfeldt-Jakob disease in R208H-V129V PRNP carrier

Genetic transmissible spongiform encephalopathy (TSE) diseases are always associated with one of the more than 50 disease-associated point or insert mutations of the PrP gene (PRNP) [12] and represent approximately 10 to 20% of all forms of TSE diseases [9]. Each mutation is often associated with specific clinic-pathological phenotype [12] that are generally represented by Creutzfeldt-Jakob disease (CJD) [3, 8], Gerstmann–Sträussler–Scheinker disease or inherited prion protein cerebral amyloidoses [5], and fatal familial insomnia [4]. The methionine/valine polymorphism at codon 129 of PRNP plays also a role in determining the disease phenotype, especially when co-segregates with the pathogenic mutation [3]. Most PRNP mutations responsible for the CJD phenotype, including the R208H, are extremely rare and often there is no evidence of CJD in other family members. In particular, the R208H mutation co-segregates either with methionine or valine at codon 129 and it has been fully described in only 12 patients carrying M129 and 4 patients with V129 [8]. Here, we report clinical and neuropathological details of the fourth worldwide case of CJD carrying the rare R208H-129 Val PRNP genotype with a suggestive positive family history for dementia