Journal ArticleDOI
Ultrasound Image Segmentation: A Deeply Supervised Network With Attention to Boundaries
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TLDR
A fully convolutional neural network with attentional deep supervision for the automatic and accurate segmentation of the ultrasound images with improvement in overall segmentation accuracy is developed.Abstract:
Objective: Segmentation of anatomical structures in ultrasound images requires vast radiological knowledge and experience. Moreover, the manual segmentation often results in subjective variations, therefore, an automatic segmentation is desirable. We aim to develop a fully convolutional neural network (FCNN) with attentional deep supervision for the automatic and accurate segmentation of the ultrasound images. Method: FCNN/CNNs are used to infer high-level context using low-level image features. In this paper, a sub-problem specific deep supervision of the FCNN is performed. The attention of fine resolution layers is steered to learn object boundary definitions using auxiliary losses, whereas coarse resolution layers are trained to discriminate object regions from the background. Furthermore, a customized scheme for downweighting the auxiliary losses and a trainable fusion layer are introduced. This produces an accurate segmentation and helps in dealing with the broken boundaries, usually found in the ultrasound images. Results: The proposed network is first tested for blood vessel segmentation in liver images. It results in $F1$ score, mean intersection over union, and dice index of 0.83, 0.83, and 0.79, respectively. The best values observed among the existing approaches are produced by U-net as 0.74, 0.81, and 0.75, respectively. The proposed network also results in dice index value of 0.91 in the lumen segmentation experiments on MICCAI 2011 IVUS challenge dataset, which is near to the provided reference value of 0.93. Furthermore, the improvements similar to vessel segmentation experiments are also observed in the experiment performed to segment lesions. Conclusion: Deep supervision of the network based on the input-output characteristics of the layers results in improvement in overall segmentation accuracy. Significance: Sub-problem specific deep supervision for ultrasound image segmentation is the main contribution of this paper. Currently the network is trained and tested for fixed size inputs. It requires image resizing and limits the performance in small size images.read more
Citations
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Journal ArticleDOI
Synthetic MRI-aided multi-organ segmentation on male pelvic CT using cycle consistent deep attention network.
Xue Dong,Yang Lei,Sibo Tian,Tonghe Wang,Pretesh Patel,Walter J. Curran,Ashesh B. Jani,Tian Liu,Xiaofeng Yang +8 more
TL;DR: A sMRI-aided multi-organ automatic segmentation method on pelvic CT images that provides accurate and consistent prostate, bladder and rectum segmentation, and has the potential to facilitate routine prostate-cancer radiotherapy treatment planning is proposed.
Journal ArticleDOI
CT prostate segmentation based on synthetic MRI-aided deep attention fully convolution network.
Yang Lei,Xue Dong,Zhen Tian,Yingzi Liu,Sibo Tian,Tonghe Wang,Xiaojun Jiang,Pretesh Patel,Ashesh B. Jani,Hui Mao,Walter J. Curran,Tian Liu,Xiaofeng Yang +12 more
TL;DR: A novel CT-only prostate segmentation strategy using CT-based sMRI, and validated its accuracy against the prostate contours that were manually drawn on MRI images and deformed to CT images, could provide accurate prostate volume for treatment planning without requiring MRI acquisition, greatly facilitating the routine clinical workflow.
Journal ArticleDOI
Male Pelvic Multi-Organ Segmentation Aided by CBCT-based Synthetic MRI
Yang Lei,Tonghe Wang,Sibo Tian,Xue Dong,Ashesh B. Jani,David M. Schuster,Walter J. Curran,Pretesh Patel,Tian Liu,Xiaofeng Yang +9 more
TL;DR: A novel CBCT-only prostate segmentation strategy usingCBCT-based sMRI and validated its accuracy against pelvic multi-organ contours that were manually drawn on MR images and deformed to CT images, greatly facilitating routine clinical workflow.
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Synthetic CT generation from non-attenuation corrected PET images for whole-body PET imaging.
Xue Dong,Tonghe Wang,Yang Lei,Kristin Higgins,Tian Liu,Walter J. Curran,Hui Mao,Jonathon A. Nye,Xiaofeng Yang +8 more
TL;DR: A deep learning-based approach to generate synthetic CT from whole-body NAC PET for PET AC shows great similarity to true CT images both qualitatively and quantitatively, and demonstrates great potential for whole- body PET attenuation correction in the absence of structural information.
References
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