•CNN based framework for cell instance segmentation and tracking.•Incorporating temporal information as ConvGRUs into a fully convolutional network.•Embedding loss based on cosine similarities to ...represent individual instances.•Extensive evaluation on muscle fiber and celltracking microscopy images.•State-of-the-art performance on six datasets of the ISBI celltracking challenge.
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Differently to semantic segmentation, instance segmentation assigns unique labels to each individual instance of the same object class. In this work, we propose a novel recurrent fully convolutional network architecture for tracking such instance segmentations over time, which is highly relevant, e.g., in biomedical applications involving cell growth and migration. Our network architecture incorporates convolutional gated recurrent units (ConvGRU) into a stacked hourglass network to utilize temporal information, e.g., from microscopy videos. Moreover, we train our network with a novel embedding loss based on cosine similarities, such that the network predicts unique embeddings for every instance throughout videos, even in the presence of dynamic structural changes due to mitosis of cells. To create the final tracked instance segmentations, the pixel-wise embeddings are clustered among subsequent video frames by using the mean shift algorithm. After showing the performance of the instance segmentation on a static in-house dataset of muscle fibers from H&E-stained microscopy images, we also evaluate our proposed recurrent stacked hourglass network regarding instance segmentation and tracking performance on six datasets from the ISBI celltracking challenge, where it delivers state-of-the-art results.
The rise of digitization promotes the development of screening and decision support tools. We sought to validate the results from a machine learning based dysphagia risk prediction tool with clinical ...evaluation.
149 inpatients in the ENT department were evaluated in real time by the risk prediction tool, as well as clinically over a 3-week period. Patients were classified by both as patients at risk/no risk.
The AUROC, reflecting the discrimination capability of the algorithm, was 0.97. The accuracy achieved 92.6% given an excellent specificity as well as sensitivity of 98% and 82.4% resp. Higher age, as well as male sex and the diagnosis of oropharyngeal malignancies were found more often in patients at risk of dysphagia.
The proposed dysphagia risk prediction tool proved to have an outstanding performance in discriminating risk from no risk patients in a prospective clinical setting. It is likely to be particularly useful in settings where there is a lower incidence of patients with dysphagia and less awareness among staff.
Objectives
The aim of this prospective study was to examine the effects of transcutaneous functional electrical stimulation (FES) in a group of elderly women with presbyphonia.
Study Design
...Prospective randomized study.
Methods
Fourteen participants were enrolled prospectively and attributed randomly to two different treatment groups, where one group (n = 7) received 8 weeks of training (5 days a week), whereas the other group (n = 7) received 4 weeks of ineffective stimulation, followed by 4 weeks of effective training. Stimulation protocols were established during baseline examination and confirmed with endoscopy to ensure a glottal reaction. Numerous acoustical, vocal, patient‐centered, and respiratory parameters were obtained at several time points.
Results
Neither 4 weeks nor 8 weeks of functional electrical transcutaneous stimulation led to changes of vocal, acoustical, or respiratory parameters, apart from patient‐centered items (Voice Handicap Index 12, Voice‐Related Quality of Life), which improved over time. However, there were no differences between the two arms for both items.
Conclusions
Transcutaneous FES over 4 weeks and 8 weeks did not lead to significantly improved objective voice and acoustical parameters, which could be caused by the fact that the muscles of interest cannot be targeted specifically enough. However, we found a significant improvement of subjective voice perception and voice‐related quality of life in both groups. We explain this finding with an observer‐expectancy effect secondary to the very time‐consuming and elaborate study procedures.
Level of Evidence
1b Laryngoscope, 130:E662–E666, 2020
Voice problems that arise during everyday vocal use can hardly be captured by standard outpatient voice assessments. In preparation for a digital health application to automatically assess ...longitudinal voice data ‘in the wild’ – the VocDoc, the aim of this paper was to study vocal fatigue from the speaker’s perspective, the healthcare professional’s perspective, and the ‘machine’s’ perspective.
We collected data of four voice healthy speakers completing a 90-min reading task. Every 10 min the speakers were asked about subjective voice characteristics. Then, we elaborated on the task of elapsed speaking time recognition: We carried out listening experiments with speech and language therapists and employed random forests on the basis of extracted acoustic features. We validated our models speaker-dependently and speaker-independently and analysed underlying feature importances. For an additional, clinical application-oriented scenario, we extended our dataset for lecture recordings of another two speakers.
Self- and expert-assessments were not consistent. With mean F1 scores up to 0.78, automatic elapsed speaking time recognition worked reliably in the speaker-dependent scenario only. A small set of acoustic features – other than features previously reported to reflect vocal fatigue – was found to universally describe long-term variations of the voice.
Vocal fatigue seems to have individual effects across different speakers. Machine learning has the potential to automatically detect and characterise vocal changes over time.
Our study provides technical underpinnings for a future mobile solution to objectively capture pathological long-term voice variations in everyday life settings and make them clinically accessible.
•A few acoustic features seem to universally describe vocal fatigue.•Vocal fatigue has rather individual effects across different speakers.•Machine learning has the potential to automatically detect effects of vocal fatigue.•A mobile app can capture clinically relevant long-term voice variations in the wild.
Purpose
Functional electrical stimulation (FES) is considered an upcoming treatment modality for a number of laryngeal diseases. However, sound data are scarce when it comes to surface FES to treat ...voice disorders. Aim of the present study was to identify and differentiate suitable surface FES patterns to activate internal laryngeal muscles.
Methods
Non-invasive FES was performed in a cohort of 17 elderly woman. Our user-customized electrical stimulation setup allowed us to deliver ten different stimulation patterns (rectangular and sawtooth shaped) with variation of frequency and amplitude. Stimulation outcome, i.e., vocal fold (VF) reaction, was continuously verified by transnasal endoscopy.
Results
Responses to FES using ten different stimulation patterns varied inter-individually. None of the stimulation parameter sets could elicit a VF reaction in all participants.
Conclusion
Based on our findings we conclude that individual fitting is necessary when defining surface stimulation parameters. To overcome limitations of previous studies, devices with freely programmable patterns are required as shown here. Endoscopic control of VF reaction is absolutely essential to ensure effectiveness of the delivered patterns.
Speech-language pathologists (SLPs) work with patients after total laryngectomy (TL) to regain verbal communication. The influence of the quality of the therapeutic relationship on the success of TL ...voice rehabilitation in terms of speech intelligibility is not known. Finding each other likeable is an important factor in establishing and maintaining interpersonal relationships in everyday life. The fit of therapist and client is relevant to the therapeutic relationship. The purpose of this study therefore was to assess the association between the degree of SLPs' likeability ratings and postlaryngectomy speech intelligibility.
In a multicentre prospective cohort study, participants rated their SLPs' likeability after finishing TL rehabilitation. Speech intelligibility was measured objectively with the Post-Laryngectomy Telephone Intelligibility Test and subjectively with the Questionnaire for Adjustment after Laryngectomy. The association of SLPs' likeability with speech intelligibility was analysed using hierarchical logistic regression, expressed with odds ratios (OR) with corresponding 95% confidence intervals (CI).
Altogether 124 patients from 13 institutions participated. The degree of finding the SLP likeable was not significantly associated with objective speech intelligibility (OR 1.30; 95% CI 0.78-2.18; p = 0.32) or subjective speech intelligibility (OR 1.01; 95% CI 0.60-1.72; p = 0.96) after controlling for age, sex and education factors.
In this patient cohort, there was no evidence for an association between ratings of SLPs' likeability and speech intelligibility outcomes after rehabilitation. Future studies could consider the use of alternative instruments for measuring likeability.
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