Automatic segmentation of lesion from breast DCE-MR image using artificial fish swarm optimization algorithm

Janaki, Sathya D.; Geetha, K.

doi:10.1515/pjmpe-2017-0006

Cited by 6 publications

References 23 publications

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A self‐supervised strategy for fully automatic segmentation of renal dynamic contrast‐enhanced magnetic resonance images

Huang

Wang

et al. 2019

Medical Physics

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Purpose An automated accurate segmentation for dynamic contrast‐enhanced magnetic resonance (DCE‐MR) image sequences is essential for quantification of renal function. A self‐supervised strategy is proposed for fully automatic segmentation of the renal DCE‐MR images without using manually labeled data. Methods The proposed strategy employed both temporal and spatial information of the DCE‐MR image sequences. First, the kidney area, the seed regions of the cortex, the medulla, and the pelvis were automatically detected in the spatial domain. Subsequently, all the pixels in the kidney were automatically labeled as the cortex, the medulla, or the pelvis based on their time–intensity signal and spatial position using a supervised classifier. The feasibility of the proposed strategy was verified on a dataset of renal DCE‐MR images of 14 subjects without history of kidney diseases. Furthermore, the self‐supervised strategy and the commonly used traditional unsupervised method were quantitatively compared with a reference manual segmentation by an experienced radiologist, using similarity indexes. Results The average Dice coefficient (ADC) for the segmentations of the proposed self‐supervised method is 0.92 using a ransom walker model as the classifier or 0.86 using a K‐nearest neighbor model as the classifier. The ADC of the Kmeans‐based unsupervised methods with three and six clusters were 0.65 and 0.79, respectively. The Dice coefficients of the self‐supervised method were remarkably higher than that of the unsupervised method (one‐tailed paired‐sample t‐test, P‐values <10−3). Conclusions The results indicate that the proposed self‐supervised approach yields a satisfactory similarity with the reference manual segmentation. Compared with the traditional unsupervised clustering method, the new strategy does not require manual intervention during the segmentation process and achieves better results for the segmentation of renal DCE‐MR images.

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