Adam Buchwald

Yael Segal, Kasia Hitczenko, Matthew Goldrick et al.

Diadochokinetic speech tasks (DDK), in which participants repeatedly produce syllables, are commonly used as part of the assessment of speech motor impairments. These studies rely on manual analyses that are time-intensive, subjective, and provide only a coarse-grained picture of speech. This paper presents two deep neural network models that automatically segment consonants and vowels from unannotated, untranscribed speech. Both models work on the raw waveform and use convolutional layers for feature extraction. The first model is based on an LSTM classifier followed by fully connected layers, while the second model adds more convolutional layers followed by fully connected layers. These segmentations predicted by the models are used to obtain measures of speech rate and sound duration. Results on a young healthy individuals dataset show that our LSTM model outperforms the current state-of-the-art systems and performs comparably to trained human annotators. Moreover, the LSTM model also presents comparable results to trained human annotators when evaluated on unseen older individuals with Parkinson's Disease dataset.

Andrew M Birnbaum, Adam Buchwald, Peter Turkeltaub et al.

Purpose: The goal of this work was to develop a deep network for whole-head segmentation including clinical MRIs with abnormal anatomy, and compile the first public benchmark dataset for this purpose. We collected 98 MRIs with volumetric segmentation labels for a diverse set of human subjects including normal, as well as abnormal anatomy in clinical cases of stroke and disorders of consciousness. Approach: Training labels were generated by manually correcting initial automated segmentations for skin/scalp, skull, CSF, gray matter, white matter, air cavity and extracephalic air. We developed a MultiAxial network consisting of three 2D U-Net that operate independently in sagittal, axial and coronal planes and are then combined to produce a single 3D segmentation. Results: The MultiAxial network achieved a test-set Dice scores of 0.88+-0.04 (median +- interquartile range) on whole head segmentation including gray and white matter. This compared to 0.86 +- 0.04 for Multipriors and 0.79 +- 0.10 for SPM12, two standard tools currently available for this task. The MultiAxial network gains in robustness by avoiding the need for coregistration with an atlas. It performed well in regions with abnormal anatomy and on images that have been de-identified. It enables more accurate and robust current flow modeling when incorporated into ROAST, a widely-used modeling toolbox for transcranial electric stimulation.Conclusions: We are releasing a new state-of-the-art tool for whole-head MRI segmentation in abnormal anatomy, along with the largest volume of labeled clinical head MRIs including labels for non-brain structures. Together the model and data may serve as a benchmark for future efforts.