Methods for the frugal labeler: Multi-class semantic segmentation on heterogeneous labels

Deep learning increasingly accelerates biomedical research, deploying neural networks for multiple tasks, such as image classification, object detection, and semantic segmentation. However, neural networks are commonly trained supervised on large-scale, labeled datasets. These prerequisites raise issues in biomedical image recognition, as datasets are generally small-scale, challenging to obtain, expensive to label, and frequently heterogeneously labeled. Furthermore, heterogeneous labels are a challenge for supervised methods. If not all classes are labeled for an individual sample, supervised deep learning approaches can only learn on a subset of the dataset with common labels for each individual sample; consequently, biomedical image recognition engineers need to be frugal concerning their label and ground truth requirements. This paper discusses the effects of frugal labeling and proposes to train neural networks for multi-class semantic segmentation on heterogeneously labeled data based on a novel objective function. The objective function combines a class asymmetric loss with the Dice loss. The approach is demonstrated for training on the sparse ground truth of a heterogeneous labeled dataset, training within a transfer learning setting, and the use-case of merging multiple heterogeneously labeled datasets. For this purpose, a biomedical small-scale, multi-class semantic segmentation dataset is utilized. The heartSeg dataset is based on the medaka fish’s position as a cardiac model system. Automating image recognition and semantic segmentation enables high-throughput experiments and is essential for biomedical research. Our approach and analysis show competitive results in supervised training regimes and encourage frugal labeling within biomedical image recognition

Automated phenotype pattern recognition of zebrafish for high-throughput screening

Over the last years, the zebrafish (Danio rerio) has become a key model organism in genetic and chemical screenings. A growing number of experiments and an expanding interest in zebrafish research makes it increasingly essential to automatize the distribution of embryos and larvae into standard microtiter plates or other sample holders for screening, often according to phenotypical features. Until now, such sorting processes have been carried out by manually handling the larvae and manual feature detection. Here, a prototype platform for image acquisition together with a classification software is presented. Zebrafish embryos and larvae and their features such as pigmentation are detected automatically from the image. Zebrafish of 4 different phenotypes can be classified through pattern recognition at 72 h post fertilization (hpf), allowing the software to classify an embryo into 2 distinct phenotypic classes: wild-type versus variant. The zebrafish phenotypes are classified with an accuracy of 79–99% without any user interaction. A description of the prototype platform and of the algorithms for image processing and pattern recognition is presented

Portable auricular device for real-time swallow and chew detection

Monitoring a person’s nutritional consumption is costly and complex. To solve this problem a new technique is proposed to draw conclusions of a person’s food intake. The air pressure signal, recorded in the external acoustic meatus, is used to detect swallow and chew events. A portable device has been developed to record this pressure signal. Due to the constraint of running on a low-power microcontroller, real-time algorithms, used in pattern and speech recognition, were used to develop methods to automatically detect swallow and chew events. A binary classifier was trained by means of manually annotated data sets. Direct comparisons with state of the art technology and tests with several subjects are provided for evaluation purposes

Repeated Training with Augmentative Vibrotactile Feedback Increases Object Manipulation Performance

Most users of prosthetic hands must rely on visual feedback alone, which requires visual attention and cognitive resources. Providing haptic feedback of variables relevant to manipulation, such as contact force, may thus improve the usability of prosthetic hands for tasks of daily living. Vibrotactile stimulation was explored as a feedback modality in ten unimpaired participants across eight sessions in a two-week period. Participants used their right index finger to perform a virtual object manipulation task with both visual and augmentative vibrotactile feedback related to force. Through repeated training, participants were able to learn to use the vibrotactile feedback to significantly improve object manipulation. Removal of vibrotactile feedback in session 8 significantly reduced task performance. These results suggest that vibrotactile feedback paired with training may enhance the manipulation ability of prosthetic hand users without the need for more invasive strategies

A brain-computer interface with vibrotactile biofeedback for haptic information

<p>Abstract</p> <p>Background</p> <p>It has been suggested that Brain-Computer Interfaces (BCI) may one day be suitable for controlling a neuroprosthesis. For closed-loop operation of BCI, a tactile feedback channel that is compatible with neuroprosthetic applications is desired. Operation of an EEG-based BCI using only <it>vibrotactile feedback</it>, a commonly used method to convey haptic senses of contact and pressure, is demonstrated with a high level of accuracy.</p> <p>Methods</p> <p>A Mu-rhythm based BCI using a motor imagery paradigm was used to control the position of a virtual cursor. The cursor position was shown visually as well as transmitted haptically by modulating the intensity of a vibrotactile stimulus to the upper limb. A total of six subjects operated the BCI in a two-stage targeting task, receiving only vibrotactile biofeedback of performance. The location of the vibration was also systematically varied between the left and right arms to investigate location-dependent effects on performance.</p> <p>Results and Conclusion</p> <p>Subjects are able to control the BCI using only vibrotactile feedback with an average accuracy of 56% and as high as 72%. These accuracies are significantly higher than the 15% predicted by random chance if the subject had no voluntary control of their Mu-rhythm. The results of this study demonstrate that vibrotactile feedback is an effective biofeedback modality to operate a BCI using motor imagery. In addition, the study shows that placement of the vibrotactile stimulation on the biceps ipsilateral or contralateral to the motor imagery introduces a significant bias in the BCI accuracy. This bias is consistent with a drop in performance generated by stimulation of the contralateral limb. Users demonstrated the capability to overcome this bias with training.</p

Separability of neural responses to standardised mechanical stimulation of limbs

Abstract Considerable scientific and technological efforts are currently being made towards the development of neural prostheses. Understanding how the peripheral nervous system responds to electro-mechanical stimulation of the limb, will help to inform the design of prostheses that can restore function or accelerate recovery from injury to the sensory motor system. However, due to differences in experimental protocols, it is difficult, if not impossible, to make meaningful comparisons between different peripheral nerve interfaces. Therefore, we developed a low-cost electronic system to standardise the mechanical stimulation of a rat’s hindpaw. Three types of mechanical stimulations, namely, proprioception, touch and nociception were delivered to the limb and the electroneurogram signals were recorded simultaneously from the sciatic nerve with a 16-contact cuff electrode. For the first time, results indicate separability of neural responses according to stimulus type as well as intensity. Statistical analysis reveal that cuff contacts placed circumferentially, rather than longitudinally, are more likely to lead to higher classification rates. This flexible setup may be readily adapted for systematic comparison of various electrodes and mechanical stimuli in rodents. Hence, we have made its electro-mechanical design and computer programme available onlin