MRI and CT images have been routinely used in clinical practice for treatment planning of the head-and-neck (HAN) radiotherapy. Delineating organs-at-risk (OAR) is an essential step in radiotherapy, however, it is tim...
详细信息
MRI and CT images have been routinely used in clinical practice for treatment planning of the head-and-neck (HAN) radiotherapy. Delineating organs-at-risk (OAR) is an essential step in radiotherapy, however, it is time-consuming and prone to inter-observer variation. The existing automatic segmentation approaches are either limited by image registration or lack of global spatial awareness, thus under-performed when dealing with segmentation of complex anatomies. Herein, we propose a full-scale attention network (FSANet) that integrates bi-side skip connections, full-scale feature fusion modules (FFM), a feature pyramid fusion and supervision module (FPFSM) to accurately and efficiently delineate OARs in HAN region on CT and MRI scans. Specifically, bi-side skip connections were adopted to keep small targets in the deep network and to capture semantic features at different scales. The FFM with cascaded attention mechanisms were used to recalibrate the significant channels and salient regions in the feature maps. The FPFSM was used to guide the network to learn the hierarchical representation so as to improve the segmentation robustness. The proposed algorithm was validated on the public benchmark HAN CT dataset and an in-house MR dataset. Both results show significant improvement compared to state-of-the-art OAR single-stage segmentation methods for the HAN region.
The gradient histogram has unimodality. It has been found from the statistics of the gradient histogram for large numbers of images that the gradient histograms of the object and background can be fitted with the X-2 ...
详细信息
ISBN:
(纸本)081944278X
The gradient histogram has unimodality. It has been found from the statistics of the gradient histogram for large numbers of images that the gradient histograms of the object and background can be fitted with the X-2 distribution density function of different degrees of freedom. A method of automatic estimation of the threshold of gradient image segmentation is presented and proved.
Many medical image segmentation methods require the selection of seed points inside the target structure. Often times, the location of these seed points determines the accuracy of the resulting target structure deline...
详细信息
Many medical image segmentation methods require the selection of seed points inside the target structure. Often times, the location of these seed points determines the accuracy of the resulting target structure delineation and may lead to undesirably high delineation variability. We present Robust-Seed, a new method for automatically reducing the variability of manual and semi-automatic seed-based segmentation methods with respect to the seed point location without compromising the target structure segmentation accuracy. The inputs are a volumetric image, a seed point inside the target structure, and a seed-based segmentation method. The output is a new seed point that optimises the target structure segmentation result. The algorithm iteratively computes a new seed point location that improves the expected target structure segmentation for the given method. Experimental evaluation of seed-based fast-marching level-set and adaptive region growing segmentation of the kidney and the liver on 32 CT scans with ground-truth delineations shows that Robust-Seed yields a perfect robustness score with no significant compromise on the segmentation quality (paired t-test, p < 0.05). The key advantages of Robust-Seed are that it is automatic, that it is independent of target structure and segmentation method used, and that it applies to a wide class of anatomical structures and clinical tasks.
暂无评论