23 June 2017 Automatic detection and recognition of multiple macular lesions in retinal optical coherence tomography images with multi-instance multilabel learning
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Abstract
Detection and recognition of macular lesions in optical coherence tomography (OCT) are very important for retinal diseases diagnosis and treatment. As one kind of retinal disease (e.g., diabetic retinopathy) may contain multiple lesions (e.g., edema, exudates, and microaneurysms) and eye patients may suffer from multiple retinal diseases, multiple lesions often coexist within one retinal image. Therefore, one single-lesion-based detector may not support the diagnosis of clinical eye diseases. To address this issue, we propose a multi-instance multilabel-based lesions recognition (MIML-LR) method for the simultaneous detection and recognition of multiple lesions. The proposed MIML-LR method consists of the following steps: (1) segment the regions of interest (ROIs) for different lesions, (2) compute descriptive instances (features) for each lesion region, (3) construct multilabel detectors, and (4) recognize each ROI with the detectors. The proposed MIML-LR method was tested on 823 clinically labeled OCT images with normal macular and macular with three common lesions: epiretinal membrane, edema, and drusen. For each input OCT image, our MIML-LR method can automatically identify the number of lesions and assign the class labels, achieving the average accuracy of 88.72% for the cases with multiple lesions, which better assists macular disease diagnosis and treatment.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Leyuan Fang, Leyuan Fang, Liumao Yang, Liumao Yang, Shutao Li, Shutao Li, Hossein Rabbani, Hossein Rabbani, Zhimin Liu, Zhimin Liu, Qinghua Peng, Qinghua Peng, Xiangdong Chen, Xiangdong Chen, } "Automatic detection and recognition of multiple macular lesions in retinal optical coherence tomography images with multi-instance multilabel learning," Journal of Biomedical Optics 22(6), 066014 (23 June 2017). https://doi.org/10.1117/1.JBO.22.6.066014 . Submission: Received: 18 April 2017; Accepted: 2 June 2017
Received: 18 April 2017; Accepted: 2 June 2017; Published: 23 June 2017
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