Max J. H. Witjes

Thomas B. Tienkamp, Teja Rebernik, Defne Abur et al.

This document outlines a PROSPERO pre-registered protocol for a systematic review regarding articulatory changes in speech following oral or orophayrngeal cancer treatment. Treatment of tumours in the oral cavity may result in physiological changes that could lead to articulatory difficulties. The tongue becomes less mobile due to scar tissue and/or potential (postoperative) radiation therapy. Moreover, tissue loss may create a bypass for airflow or limit constriction possibilities. In order to gain a better understanding of the nature of the speech problems, information regarding the movement of the articulators is needed since perceptual or acoustic information provide only indirect evidence of articulatory changes. Therefore, this systematic review will review studies that directly measured the articulatory movements of the tongue, jaw, and lips following treatment for oral or oropharyngeal cancer.

Thomas Tienkamp, Fleur van Ast, Roos van der Veen et al.

Surgical treatment for tongue cancer can negatively affect the mobility and musculature of the tongue, which can influence articulatory clarity and variability. In this study, we investigated articulatory clarity through the vowel articulation index (VAI) and variability through vowel formant dispersion (VFD). Using a sentence reading task, we assessed 11 individuals pre and six months post tongue cancer surgery, alongside 11 sex- and age matched typical speakers. Our results show that while the VAI was significantly smaller post-surgery compared to pre-surgery, there was no significant difference between patients and typical speakers at either time point. Post-surgery, speakers had higher VFD values for /i/ compared to pre-surgery and typical speakers, signalling higher variability. Taken together, our results suggest that while articulatory clarity remained within typical ranges following surgery for tongue cancer for the speakers in our study, articulatory variability increased.

Bence Mark Halpern, Teja Rebernik, Thomas Tienkamp et al.

We present an articulatory synthesis framework for the synthesis and manipulation of oral cancer speech for clinical decision making and alleviation of patient stress. Objective and subjective evaluations demonstrate that the framework has acceptable naturalness and is worth further investigation. A subsequent subjective vowel and consonant identification experiment showed that the articulatory synthesis system can manipulate the articulatory trajectories so that the synthesised speech reproduces problems present in the ground truth oral cancer speech.

Bingjiang Qiu, Jiapan Guo, Joep Kraeima et al.

Recently, accurate mandible segmentation in CT scans based on deep learning methods has attracted much attention. However, there still exist two major challenges, namely, metal artifacts among mandibles and large variations in shape or size among individuals. To address these two challenges, we propose a recurrent segmentation convolutional neural network (RSegCNN) that embeds segmentation convolutional neural network (SegCNN) into the recurrent neural network (RNN) for robust and accurate segmentation of the mandible. Such a design of the system takes into account the similarity and continuity of the mandible shapes captured in adjacent image slices in CT scans. The RSegCNN infers the mandible information based on the recurrent structure with the embedded encoder-decoder segmentation (SegCNN) components. The recurrent structure guides the system to exploit relevant and important information from adjacent slices, while the SegCNN component focuses on the mandible shapes from a single CT slice. We conducted extensive experiments to evaluate the proposed RSegCNN on two head and neck CT datasets. The experimental results show that the RSegCNN is significantly better than the state-of-the-art models for accurate mandible segmentation.

Bingjiang Qiu, Jiapan Guo, J. Kraeima et al.

Segmentation of mandibles in CT scans during virtual surgical planning is crucial for 3D surgical planning in order to obtain a detailed surface representation of the patients bone. Automatic segmentation of mandibles in CT scans is a challenging task due to large variation in their shape and size between individuals. In order to address this challenge we propose a convolutional neural network approach for mandible segmentation in CT scans by considering the continuum of anatomical structures through different planes. The proposed convolutional neural network adopts the architecture of the U-Net and then combines the resulting 2D segmentations from three different planes into a 3D segmentation. We implement such a segmentation approach on 11 neck CT scans and then evaluate the performance. We achieve an average dice coefficient of $ 0.89 $ on two testing mandible segmentation. Experimental results show that our proposed approach for mandible segmentation in CT scans exhibits high accuracy.