Spleen segmentation in MRI sequence images using template matching and active contours

Mihaylova, Antonia; Georgieva, Veska

doi:10.1016/j.procs.2018.04.180

Cited by 10 publications

(5 citation statements)

References 7 publications

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“…For example, circular or elliptical Hough transform are used in optic disc segmentation [23], [3]. Template matching is also used for spleen segmentation in MRI sequence images [24] and ventricular segmentation in brain CT images [22].…”

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confidence: 99%

CE-Net: Context Encoder Network for 2D Medical Image Segmentation

Cheng

et al. 2019

IEEE Trans. Med. Imaging

1,503

775

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Medical image segmentation is an important step in medical image analysis. With the rapid development of convolutional neural network in image processing, deep learning has been used for medical image segmentation, such as optic disc segmentation, blood vessel detection, lung segmentation, cell segmentation, etc. Previously, U-net based approaches have been proposed. However, the consecutive pooling and strided convolutional operations lead to the loss of some spatial information. In this paper, we propose a context encoder network (referred to as CE-Net) to capture more high-level information and preserve spatial information for 2D medical image segmentation. CE-Net mainly contains three major components: a feature encoder module, a context extractor and a feature decoder module. We use pretrained ResNet block as the fixed feature extractor. The context extractor module is formed by a newly proposed dense atrous convolution (DAC) block and residual multi-kernel pooling (RMP) block. We applied the proposed CE-Net to different 2D medical image segmentation tasks. Comprehensive results show that the proposed method outperforms the original U-Net method and other state-of-the-art methods for optic disc segmentation, vessel detection, lung segmentation, cell contour segmentation and retinal optical coherence tomography layer segmentation.

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confidence: 99%

CE-Net: Context Encoder Network for 2D Medical Image Segmentation

Cheng

et al. 2019

IEEE Trans. Med. Imaging

1,503

775

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“…In the past, medical image segmentation often uses hand-made features for segmentation [ 18 – 20 ]. With the rapid progress of DCNN, in particular, Unet with its encoder-decoder framework has come to the fore, revolutionizing deep semantic segmentation of medical images.…”

Section: Related Workmentioning

confidence: 99%

ICUnet++: an Inception-CBAM network based on Unet++ for MR spine image segmentation

Qin

et al. 2023

Int. J. Mach. Learn. & Cyber.

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In recent years, more attention paid to the spine caused by related diseases, spinal parsing (the multi-class segmentation of vertebrae and intervertebral disc) is an important part of the diagnosis and treatment of various spinal diseases. The more accurate the segmentation of medical images, the more convenient and quick the clinicians can evaluate and diagnose spinal diseases. Traditional medical image segmentation is often time consuming and energy consuming. In this paper, an efficient and novel automatic segmentation network model for MR spine images is designed. The proposed Inception-CBAM Unet++ (ICUnet++) model replaces the initial module with the Inception structure in the encoder-decoder stage base on Unet++ , which uses the parallel connection of multiple convolution kernels to obtain the features of different receptive fields during in the feature extraction. According to the characteristics of the attention mechanism, Attention Gate module and CBAM module are used in the network to make the attention coefficient highlight the characteristics of the local area. To evaluate the segmentation performance of network model, four evaluation metrics, namely intersection over union (IoU), dice similarity coefficient(DSC), true positive rate(TPR), positive predictive value(PPV) are used in the study. The published SpineSagT2Wdataset3 spinal MRI dataset is used during the experiments. In the experiment results, IoU reaches 83.16%, DSC is 90.32%, TPR is 90.40%, and PPV is 90.52%. It can be seen that the segmentation indicators have been significantly improved, which reflects the effectiveness of the model.

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“…Though there were a range of later adaptations, such as introducing residual blocks [27] and dense blocks [28] to the classic U-Net architecture, [29] suggested that with careful pre-processing and hyperparameter tuning, U-Net remained a very competitive network for medical image segmentation. Although automatic spleen segmentation has been attempted from CT images and magnetic resonance images (MRI) [30], automatic interpretation of splenic ultrasound images remains challenging due to the relatively poor image quality.…”

Section: Related Workmentioning

confidence: 99%