Thyroid Segmentation and Volume Estimation Using CT Images

ABSTRACT: Pathology of thyroid gland is determined by physicians with its volume as a significant indicator.For this thyroid area segmentation and volume estimation are necessary steps. Most physicians use CT images even if the volume of thyroid gland is determined using Ultrasound images, for precise evaluation of volume of thyroid gland. In this paper a Linear Vector Quantization neural network (LVQNN) with a pre-processing procedure and initial segmentation using cellular automata(CA) is proposed for thyroid segmentation and volume estimation using computerized tomography (CT) images

Tracked 3D ultrasound and deep neural network-based thyroid segmentation reduce interobserver variability in thyroid volumetry

Thyroid volumetry is crucial in the diagnosis, treatment, and monitoring of thyroid diseases. However, conventional thyroid volumetry with 2D ultrasound is highly operator-dependent. This study compares 2D and tracked 3D ultrasound with an automatic thyroid segmentation based on a deep neural network regarding inter- and intraobserver variability, time, and accuracy. Volume reference was MRI. 28 healthy volunteers (24—50 a) were scanned with 2D and 3D ultrasound (and by MRI) by three physicians (MD 1, 2, 3) with different experience levels (6, 4, and 1 a). In the 2D scans, the thyroid lobe volumes were calculated with the ellipsoid formula. A convolutional deep neural network (CNN) automatically segmented the 3D thyroid lobes. 26, 6, and 6 random lobe scans were used for training, validation, and testing, respectively. On MRI (T1 VIBE sequence) the thyroid was manually segmented by an experienced MD. MRI thyroid volumes ranged from 2.8 to 16.7ml (mean 7.4, SD 3.05). The CNN was trained to obtain an average Dice score of 0.94. The interobserver variability comparing two MDs showed mean differences for 2D and 3D respectively of 0.58 to 0.52ml (MD1 vs. 2), −1.33 to −0.17ml (MD1 vs. 3) and −1.89 to −0.70ml (MD2 vs. 3). Paired samples t-tests showed significant differences for 2D (p = .140, p = .002 and p = .002) and none for 3D (p = .176, p = .722 and p = .057). Intraobsever variability was similar for 2D and 3D ultrasound. Comparison of ultrasound volumes and MRI volumes showed a significant difference for the 2D volumetry of all MDs (p = .002, p = .009, p <.001), and no significant difference for 3D ultrasound (p = .292, p = .686, p = 0.091). Acquisition time was significantly shorter for 3D ultrasound. Tracked 3D ultrasound combined with a CNN segmentation significantly reduces interobserver variability in thyroid volumetry and increases the accuracy of the measurements with shorter acquisition times

Extraocular muscle sampled volume in Graves' orbitopathy using 3-T fast spin-echo MRI with iterative decomposition of water and fat sequences

Abstract Background: Current magnetic resonance imaging (MRI) techniques for measuring extraocular muscle (EOM) volume enlargement are not ideally suited for routine follow-up of Graves’ ophthalmopathy (GO) because the difficulty of segmenting the muscles at the tendon insertion complicates and lengthens the study protocol. Purpose: To measure the EOM sampled volume (SV) and assess its correlation with proptosis. Material and Methods: A total of 37 patients with newly diagnosed GO underwent 3-T MRI scanning with iterative decomposition of water and fat (IDEAL) sequences with and without contrast enhancement. In each patient, the three largest contiguous coronal cross-sectional areas (CSA) on the EOM slices were segmented using a polygon selection tool and then summed to compute the EOM-SV. Proptosis was evaluated with the Hertel index (HI). The relationships between the HI value and EOM-SV and between HI and EOM-CSA were compared and assessed with Pearson’s correlation coefficient and the univariate regression coefficient. Inter-observer and intra-observer variability were calculated. Results: HI showed a stronger correlation with EOM-SV (P&lt;0.001; r¼0.712, r2¼0.507) than with EOM-CSA (P&lt;0.001; r¼0.645 and r2¼0.329). The intraclass correlation coefficient indicated that the inter-observer agreement was high (0.998). The standard deviation between repeated measurements was 1.9–5.3%. Conclusion: IDEAL sequences allow for the measurement EOM-SV both on non-contrast and contrast-enhanced scans. EOM-SV predicts proptosis more accurately than does EOM-CSA. The measurement of EOM-SV is practical and reproducible. EOM-SV changes of 3.5–8.3% can be assumed to reflect true volume changes

A Survey on Thyroid Ultrasound Image Analysis

Ultrasound imaging plays a prominent role in the diagnosis of thyroid gland.Imaging helps to detect and classify the abnormalities of thyroid gland.This survey focuses on thyroid ultrasound image features that are important for diagnosis.Various researchers have developed different techniques to detect and classify the thyroid nodules.A brief survey of various techniques developed for the analysis of thyroid ultrasound images is carried out in this paper

On the Localization of Ultrasound Image Slices within Point Distribution Models

Thyroid disorders are most commonly diagnosed using high-resolution Ultrasound (US). Longitudinal nodule tracking is a pivotal diagnostic protocol for monitoring changes in pathological thyroid morphology. This task, however, imposes a substantial cognitive load on clinicians due to the inherent challenge of maintaining a mental 3D reconstruction of the organ. We thus present a framework for automated US image slice localization within a 3D shape representation to ease how such sonographic diagnoses are carried out. Our proposed method learns a common latent embedding space between US image patches and the 3D surface of an individual's thyroid shape, or a statistical aggregation in the form of a statistical shape model (SSM), via contrastive metric learning. Using cross-modality registration and Procrustes analysis, we leverage features from our model to register US slices to a 3D mesh representation of the thyroid shape. We demonstrate that our multi-modal registration framework can localize images on the 3D surface topology of a patient-specific organ and the mean shape of an SSM. Experimental results indicate slice positions can be predicted within an average of 1.2 mm of the ground-truth slice location on the patient-specific 3D anatomy and 4.6 mm on the SSM, exemplifying its usefulness for slice localization during sonographic acquisitions. Code is publically available: \href{https://github.com/vuenc/slice-to-shape}{https://github.com/vuenc/slice-to-shape}Comment: ShapeMI Workshop @ MICCAI 2023; 12 pages 2 figure

Thyroid Nodule Image Analysis using Morphological Segmentation

Computer-aided investigative processing has become an important part of medical practice. New growth of high expertise and use of a choice of imaging modalities, more confront arise so that high rate information can be produced for disease finding and behavior. Ultrasonography of Thyroid gland is the most common, portable, widely accessible, cheap, painless and secure. It is used to distinct the thyroid nodule images that are classified into two categories: (i) benign thyroid ample, (ii) malignant lump of thyroid gland. In this paper, Mathematical Morphology is used to segment the thyroid region and measure the area, perimeter, width and height of the thyroid area. Thyroid nodule images are taken from twenty peoples as samples.Keywords— Thyroid, Morphological operation, Ultrasound, Segmentation, Tumo

Recent Advances in Machine Learning Applied to Ultrasound Imaging

Machine learning (ML) methods are pervading an increasing number of fields of application because of their capacity to effectively solve a wide variety of challenging problems. The employment of ML techniques in ultrasound imaging applications started several years ago but the scientific interest in this issue has increased exponentially in the last few years. The present work reviews the most recent (2019 onwards) implementations of machine learning techniques for two of the most popular ultrasound imaging fields, medical diagnostics and non-destructive evaluation. The former, which covers the major part of the review, was analyzed by classifying studies according to the human organ investigated and the methodology (e.g., detection, segmentation, and/or classification) adopted, while for the latter, some solutions to the detection/classification of material defects or particular patterns are reported. Finally, the main merits of machine learning that emerged from the study analysis are summarized and discussed. © 2022 by the authors. Licensee MDPI, Basel, Switzerland