Echocardiography Image Segmentation: A Survey

Mazaheri, Samaneh; Sulaiman, Puteri Suhaiza; Rahmat, Rahmita Wirza O. K.; Khalid, Fatimah; Kadiman, Suhaini; Dimon, Mohd Zamrin; Tayebi, Rohollah Moosavi

doi:10.1109/acsat.2013.71

Cited by 24 publications

(10 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…4 Traditional segmentation methodsbased on active contours, level-sets, and active shape modelshave achieved limited success for cardiac ultrasound image segmentation. [5][6][7] The segmentation accuracy has been limited by various challenges to ultrasound imaging, for example, poor contrast, inhomogeneous brightness, low signal-to-noise ratio, varying speckle noise, edge dropout, and shadows cast by dense muscle and ribs. 8 Furthermore, the variations in tissue echogenicity, cardiac shape, and motion across a patient population pose additional challenges.…”

Section: Introductionmentioning

confidence: 99%

Echocardiographic image multi‐structure segmentation using Cardiac‐SegNet

Lei

Roper

et al. 2021

Medical Physics

View full text Add to dashboard Cite

Purpose: Cardiac boundary segmentation of echocardiographic images is important for cardiac function assessment and disease diagnosis. However, it is challenging to segment cardiac ventricles due to the low contrast-to-noise ratio and speckle noise of the echocardiographic images. Manual segmentation is subject to interobserver variability and is too slow for real-time image-guided interventions. We aim to develop a deep learning-based method for automated multi-structure segmentation of echocardiographic images. Methods: We developed an anchor-free mask convolutional neural network (CNN), termed Cardiac-SegNet, which consists of three subnetworks, that is, a backbone, a fully convolutional one-state object detector (FCOS) head, and a mask head. The backbone extracts multi-level and multi-scale features from endocardium image. The FOCS head utilizes these features to detect and label the region-of-interests (ROIs) of the segmentation targets. Unlike the traditional mask regional CNN (Mask R-CNN) method, the FCOS head is anchor-free and can model the spatial relationship of the targets. The mask head utilizes a spatial attention strategy, which allows the network to highlight salient features to perform segmentation on each detected ROI. For evaluation, we investigated 450 patient datasets by a five-fold cross-validation and a hold-out test. The endocardium (LV Endo ) and epicardium (LV Epi ) of the left ventricle and left atrium (LA) were segmented and compared with manual contours using the Dice similarity coefficient (DSC), Hausdorff distance (HD), mean absolute distance (MAD), and center-of-mass distance (CMD). Results: Compared to U-Net and Mask R-CNN, our method achieved higher segmentation accuracy and fewer erroneous speckles. When our method was evaluated on a separate hold-out dataset at the end diastole (ED) and the end systole (ES) phases, the average DSC were 0.952 and 0.939 at ED and ES for the LV Endo , 0.965 and 0.959 at ED and ES for the LV Epi , and 0.924 and 0.926 at ED and ES for the LA. For patients with a typical image size of 549 × 788 pixels, the proposed method can perform the segmentation within 0.5 s. Conclusion:We proposed a fast and accurate method to segment echocardiographic images using an anchor-free mask CNN.

show abstract

Section: Introductionmentioning

confidence: 99%

Echocardiographic image multi‐structure segmentation using Cardiac‐SegNet

Lei

Roper

et al. 2021

Medical Physics

View full text Add to dashboard Cite

show abstract

“…U2S is often specified in the segmentation task. It is addressed with the following methods: Level set (LS) [1] segmentation, Deformable templates [2,3], Active shape models (ASM) [4,5], Active contour methods, Active appearance models (AAM), Bottom-up approaches, and Database-guided (DB-guided) segmentation. LS and deformable templates present some drawbacks regarding the prior knowledge included in the optimization function.…”

Section: Introductionmentioning

confidence: 99%

Interactive Echocardiography Translation Using Few-Shot GAN Transfer Learning

Teng

Zhang

Qian

et al. 2020

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

Background. Interactive echocardiography translation is an efficient educational function to master cardiac anatomy. It strengthens the student’s understanding by pixel-level translation between echocardiography and theoretically sketch images. Previous research studies split it into two aspects of image segmentation and synthesis. This split makes it hard to achieve pixel-level corresponding translation. Besides, it is also challenging to leverage deep-learning-based methods in each phase where a handful of annotations are available. Methods. To address interactive translation with limited annotations, we present a two-step transfer learning approach. Firstly, we train two independent parent networks, the ultrasound to sketch (U2S) parent network and the sketch to ultrasound (S2U) parent network. U2S translation is similar to a segmentation task with sector boundary inference. Therefore, the U2S parent network is trained with the U-Net network on the public segmentation dataset of VOC2012. S2U aims at recovering ultrasound texture. So, the S2U parent network is decoder networks that generate ultrasound data from random input. After pretraining the parent networks, an encoder network is attached to the S2U parent network to translate ultrasound images into sketch images. We jointly transfer learning U2S and S2U within the CGAN framework. Results and conclusion. Quantitative and qualitative contrast from 1-shot, 5-shot, and 10-shot transfer learning show the effectiveness of the proposed algorithm. The interactive translation is achieved with few-shot transfer learning. Thus, the development of new applications from scratch is accelerated. Our few-shot transfer learning has great potential in the biomedical computer-aided image translation field, where annotation data are extremely precious.

show abstract

“…As ultrasound (US) images are always crowded with image noises, it becomes practically challenging to segment tumors in BUS images [22,23]. Specifically, image noises that pose challenges to segment BUS images as they effectively include intensity inhomogeneity, low signal-to-noise ratio, and high speckle noise [24]. In the medical examination of image segmentation, the task is typically accomplished by labor-intensive human efforts to perform tracing.…”

Section: Introductionmentioning

confidence: 99%

A Review on Region of Interest Segmentation Based on Clustering Techniques for Breast Cancer Ultrasound Images

Muhammad

Zeebaree

Brifcani

et al. 2020

JASTT

View full text Add to dashboard Cite

The most prevalent cancer amongst women is woman breast cancer. Ultrasound imaging is a widely employed method for identifying and diagnosing breast abnormalities. Computer-aided diagnosis technologies have lately been developed with ultrasound images to help radiologists enhance the accuracy of the diagnosis. This paper presents several ultrasound image segmentation techniques, mainly focus on eight clustering methods over the last 10 years, and it shows the advantages and disadvantages of these approaches. Breast ultrasound image segmentation is, therefore, still an accessible and challenging issue due to numerous ultrasound artifacts introduced in the imaging process, including high speckle noise, poor contrast, blurry edges, weak signal-to-noise ratio, and intensity inhomogeneity.

show abstract

Echocardiography Image Segmentation: A Survey

Cited by 24 publications

References 57 publications

Echocardiographic image multi‐structure segmentation using Cardiac‐SegNet

Echocardiographic image multi‐structure segmentation using Cardiac‐SegNet

Interactive Echocardiography Translation Using Few-Shot GAN Transfer Learning

A Review on Region of Interest Segmentation Based on Clustering Techniques for Breast Cancer Ultrasound Images

Contact Info

Product

Resources

About