Comparison of thyroid segmentation techniques for 3D ultrasound

Wunderling, Tom; Golla, B.; Poudel, Prabal; Arens, Christoph; Friebe, Michael; Hansen, Christian

doi:10.1117/12.2254234

Cited by 23 publications

(20 citation statements)

References 7 publications

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“…This database has a total of 675 2D US slices with a 760 × 500 pixels with between 53 and 189 US slices per subject. The second dataset (in the sequel Dataset 2) has been presented in [25] and can be downloaded in http://opencas.webarchiv.kit.edu/?q=node/29. It involves freehand US images of 16 healthy subjects, each acquired also with a GE Logiq E9 system but operated by a different clinician in a different hospital than in the Database 1 case.…”

Section: Resultsmentioning

confidence: 99%

Parametrical modelling for texture characterization—A novel approach applied to ultrasound thyroid segmentation

et al. 2019

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Texture analysis is an important topic in Ultrasound (US) image analysis for structure segmentation and tissue classification. In this work a novel approach for US image texture feature extraction is presented. It is mainly based on parametrical modelling of a signal version of the US image in order to process it as data resulting from a dynamical process. Because of the predictive characteristics of such a model representation, good estimations of texture features can be obtained with less data than generally used methods require, allowing higher robustness to low Signal-to-Noise ratio and a more localized US image analysis. The usability of the proposed approach was demonstrated by extracting texture features for segmenting the thyroid in US images. The obtained results showed that features corresponding to energy ratios between different modelled texture frequency bands allowed to clearly distinguish between thyroid and non-thyroid texture. A simple k-means clustering algorithm has been used for separating US image patches as belonging to thyroid or not. Segmentation of thyroid was performed in two different datasets obtaining Dice coefficients over 85%.

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Section: Resultsmentioning

confidence: 99%

Parametrical modelling for texture characterization—A novel approach applied to ultrasound thyroid segmentation

et al. 2019

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“…The proposed method outperforms the recent related prior art [11] as presented in Table 1. Thyroid Segmentation: The thyroid data used in this experiment has been acquired from a publicly available dataset [3] which includes freehand acquired thyroid US volumes from 16 healthy human subjects imaged with a 11 − 16 MHz probe. The proposed approach is compared with four different algorithms which has been reported by Narayan et.…”

Section: Experiments Results and Discussionmentioning

confidence: 99%

“…This is derived as a result of mixing of the backscattered signals, thereby challenging the visual reader's ability. The challenges associated with reporting ultrasound images are primarily on account of (i) the stochastic nature of speckle intensity and the low signal-to-noise ratio leading to lowered contrast between structures [1], (ii) imaging and volume rendering artifacts introduced due to breathing and other kinds of body motion during imaging [2], (iii) motion induced artifacts during freehand scanning of 2D frames that are subsequently registered and stacked to form 3D volume [3].…”

Section: Introductionmentioning

confidence: 99%

SUMNet: Fully Convolutional Model For Fast Segmentation of Anatomical Structures in Ultrasound Volumes

Nandamuri

China

Mitra

et al. 2019

2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)

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Ultrasound imaging is generally employed for real-time investigation of internal anatomy of the human body for disease identification. Delineation of the anatomical boundary of organs and pathological lesions is quite challenging due to the stochastic nature of speckle intensity in the images, which also introduces visual fatigue for the observer. This paper introduces a fully convolutional neural network based method to segment organ and pathologies in ultrasound volume by learning the spatial-relationship between closely related classes in the presence of stochastically varying speckle intensity. We propose a convolutional encoder-decoder like framework with (i) feature concatenation across matched layers in encoder and decoder and (ii) index passing based unpooling at the decoder for semantic segmentation of ultrasound volumes. We have experimentally evaluated the performance on publicly available datasets consisting of 10 intravascular ultrasound pullback acquired at 20 MHz and 16 freehand thyroid ultrasound volumes acquired 11 − 16 MHz. We have obtained a dice score of 0.93 ± 0.08 and 0.92 ± 0.06 respectively, following a 10-fold cross-validation experiment while processing frame of 256 × 384 pixel in 0.035s and a volume of 256 × 384 × 384 voxel in 13.44s.

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“…They used radial based-NN and patch based classification is used for training and achieves the accuracy of 96.52, however the result was mainly based on selected images. In [18], developed another automatic method which showed that manual tracing is better for segmentation and does provide the better understanding of methodology due to tracing, however it requires highly experienced radiologists [19,20] developed an automatic method for segmenting the thyroid glands in 2D images, this method was named on RBFNN. Further 3D thyroid volumes were estimated based on 2D image.…”

Section: Related Workmentioning

confidence: 99%

Ultrasound image segmentation through deep learning based improvised U-Net

Shenoy

Jatti²

2021

IJEECS

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Thyroid nodule are fluid or solid lump that are formed within human’s gland and most thyroid nodule doesn’t show any symptom or any sign; moreover there are certain percentage of thyroid gland are cancerous and which could lead human into critical situation up to death. Hence, it is one of the important type of cancer and also it is important for detection of cancer. Ultrasound imaging is widely popular and frequently used tool for diagnosing thyroid cancer, however considering the wide application in clinical area such estimating size, shape and position of thyroid cancer. Further, it is important to design automatic and absolute segmentation for better detection and efficient diagnosis based on US-image. Segmentation of thyroid gland from the ultrasound image is quiet challenging task due to inhomogeneous structure and similar existence of intestine. Thyroid nodule can appear anywhere and have any kind of contrast, shape and size, hence segmentation process needs to designed carefully; several researcher have worked in designing the segmentation mechanism, however most of them were either semi-automatic or lack with performance metric, however it was suggested that U-Net possesses great accuracy. Hence, in this paper, we proposed improvised U-Net which focuses on shortcoming of U-Net, the main aim of this research work is to find the probable Region of interest and segment further. Furthermore, we develop High level and low-level feature map to avoid the low-resolution problem and information; later we develop dropout layer for further optimization. Moreover proposed model is evaluated considering the important metrics such as accuracy, Dice Coefficient, AUC, F1-measure and true positive; our proposed model performs better than the existing model.

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Comparison of thyroid segmentation techniques for 3D ultrasound

Cited by 23 publications

References 7 publications

Parametrical modelling for texture characterization—A novel approach applied to ultrasound thyroid segmentation

Parametrical modelling for texture characterization—A novel approach applied to ultrasound thyroid segmentation

SUMNet: Fully Convolutional Model For Fast Segmentation of Anatomical Structures in Ultrasound Volumes

Ultrasound image segmentation through deep learning based improvised U-Net

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