An Improved Air Tissue Boundary Segmentation Technique for Real Time Magnetic Resonance Imaging Video Using Segnet

Ca, Valliappan; Kumar, Avinash; Mannem, Renuka; Karthik, GR; Ghosh, Prasanta Kumar

doi:10.1109/icassp.2019.8683153

Cited by 19 publications

(14 citation statements)

References 22 publications

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“…The acoustic feature based method CDNN mfcc performs better than the articulatory feature based methods such as CDNNarti and CDNNiarti. Although we use the segnet model [31] for ATB prediction due to its best performance so far in the literature, the predicted upper and lower vocal tract boundaries are not as accurate as the ground truth ATBs. Thus in VTTP generation, the distance between the upper and lower vocal tract boundaries may not always be accurate enough to follow the vowel and consonant variation which could affect the final speech rate estimation.…”

Section: Methodsmentioning

confidence: 99%

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Acoustic and Articulatory Feature Based Speech Rate Estimation Using a Convolutional Dense Neural Network

et al. 2019

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In this paper, we propose a speech rate estimation approach using a convolutional dense neural network (CDNN). The CDNN based approach uses the acoustic and articulatory features for speech rate estimation. The Mel Frequency Cepstral Coefficients (MFCCs) are used as acoustic features and the articulograms representing time-varying vocal tract profile are used as articulatory features. The articulogram is computed from a real-time magnetic resonance imaging (rtMRI) video in the midsagittal plane of a subject while speaking. However, in practice, the articulogram features are not directly available, unlike acoustic features from speech recording. Thus, we use an Acoustic-to-Articulatory Inversion method using a bidirectional long-short-term memory network which estimates the articulogram features from the acoustics. The proposed CDNN based approach using estimated articulatory features requires both acoustic and articulatory features during training but it requires only acoustic data during testing. Experiments are conducted using rtMRI videos from four subjects each speaking 460 sentences. The Pearson correlation coefficient is used to evaluate the speech rate estimation. It is found that the CDNN based approach gives a better correlation coefficient than the temporal and selected sub-band correlation (TCSSBC) based baseline scheme by 81.58% and 73.68% (relative) in seen and unseen subject conditions respectively.

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Section: Methodsmentioning

confidence: 99%

“…The 30 distances are considered starting from the lips to the glottis. The upper and lower vocal tract boundary shapes (red and green curves in Figure 3) are obtained using the air-tissue boundary (ATB) segmentation technique proposed in [31]. The ATB segmentation approach uses a convolutional encoder-decoder network (Seg-Net).…”

Section: Mfcc Feature Extractionmentioning

confidence: 99%

Acoustic and Articulatory Feature Based Speech Rate Estimation Using a Convolutional Dense Neural Network

et al. 2019

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“…To enable quantitative analysis of the information provided by these images, it is necessary to segment the anatomical features of interest, such as the vocal tract and articulators [1] , [2] , [3] , [4] , [5] . To avoid the time-consuming and expensive process of manual segmentation, several methods have been developed to perform this task semi or fully automatically [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] . One of these methods segmented the entire vocal tract [25] , while the others only labelled pixels at air-tissue boundaries and therefore created a partial contour for each articulator [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] .…”

Section: Introductionmentioning

confidence: 99%

Deep-learning-based segmentation of the vocal tract and articulators in real-time magnetic resonance images of speech

Ruthven

Miquel

King

2021

Computer Methods and Programs in Biomedicine

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Highlights A method to segment the vocal tract and articulators in MR images was developed. Median accuracy: Dice coefficient of 0.92; general Hausdorff distance of 5mm. Developed to facilitate quantitative analysis of the vocal tract and articulators. Intended for use in clinical and non-clinical studies of speech. A novel clinically relevant segmentation accuracy metric was also developed.

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“…SegNet also has many applications in the field of medical image segmentation . Moreover, its application in semantic segmentation is relatively mature . PSPNet is rarely used in medical image field .…”

Section: Introductionmentioning

confidence: 99%

“…[28][29][30][31] Moreover, its application in semantic segmentation is relatively mature. [32][33][34][35] PSPNet is rarely used in medical image field. 28 However, it is currently used in semantic segmentation and remote sensing image segmentation, [36][37][38] and can also be used for image classification.…”

Section: Introductionmentioning

confidence: 99%

Automated polyp segmentation for colonoscopy images: A method based on convolutional neural networks and ensemble learning

et al. 2019

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Purpose To automatically and efficiently segment the lesion area of the colonoscopy polyp image, a polyp segmentation method has been presented. Methods An ensemble model of pretrained convolutional neural networks was proposed, using Unet‐VGG, SegNet‐VGG, and PSPNet. Firstly, the Unet‐VGG is obtained by the first 10 layers of VGG16 as the contraction path of the left half of the Unet. Then, the SegNet‐VGG is acquired by fine‐tuned transfer learning VGG16, using the first 13 layers of VGG16 as the encoder of the SegNet and combined the original decoder of the SegNet. By adjusting the input size of the Unet‐VGG, SegNet‐VGG, and PSPNet, the preprocessed data can be correctly fed to the three network models. The three models are used as the basic trainer to train and segment the datasets. Based on the ensemble learning algorithm, the weight voting method is used to ensemble the segmentation results corresponding to single basic trainer. Results Both IoU and DICE similarity score were used to evaluate the segmentation quality for cvc300 with 300 images, CVC‐ClinicDB with 612 images, and ETIS‐LaribPolypDB with 196 images. From the experimental results, the IoU and DICE obtained by the proposed method for the cvc300 datasets can reach up to 96.16% and 98.04%, respectively, the IoU and DICE for the CVC‐ClinicDB datasets can reach up to 96.66% and 98.30%, respectively, whereas the IoU and DICE for the ETIS‐LaribPolypDB datasets can reach up to 96.95% and 98.45%, respectively. Evaluation of the IoU and DICE in our methods shows higher accuracy than previous methods. Conclusions The experimental results show that the proposed method improved correspondingly in IoU and DICE compared to a single basic trainer. The range of improvement is 1.98%–6.38%. The proposed ensemble learning succeeds in automatic polyp segmentation, which potentially helps to establish more polyp datasets.

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An Improved Air Tissue Boundary Segmentation Technique for Real Time Magnetic Resonance Imaging Video Using Segnet

Cited by 19 publications

References 22 publications

Acoustic and Articulatory Feature Based Speech Rate Estimation Using a Convolutional Dense Neural Network

Acoustic and Articulatory Feature Based Speech Rate Estimation Using a Convolutional Dense Neural Network

Deep-learning-based segmentation of the vocal tract and articulators in real-time magnetic resonance images of speech

Automated polyp segmentation for colonoscopy images: A method based on convolutional neural networks and ensemble learning

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