Hunan University, College of Electrical and Information Engineering, Changsha, Hunan, China.
Isfahan University of Medical Sciences, Medical Image and Signal Processing Research Center, Isfahan, Iran.
J Biomed Opt. 2017 Jun 1;22(6):66014. doi: 10.1117/1.JBO.22.6.066014.
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.
在光学相干断层扫描（OCT）中，黄斑病变的检测和识别对于视网膜疾病的诊断和治疗非常重要。由于一种视网膜疾病（例如糖尿病性视网膜病变）可能包含多种病变（例如水肿、渗出物和微动脉瘤），并且眼疾患者可能患有多种视网膜疾病，因此，一种单一病变的检测器可能无法支持临床眼病的诊断。为了解决这个问题，我们提出了一种基于多实例多标签的病变识别（MIML-LR）方法，用于同时检测和识别多种病变。所提出的 MIML-LR 方法包括以下步骤：（1）分割不同病变的感兴趣区域（ROI），（2）计算每个病变区域的描述性实例（特征），（3）构建多标签检测器，以及（4）使用检测器识别每个 ROI。该方法在 823 张具有正常黄斑和三种常见病变（如视网膜前膜、水肿和玻璃膜疣）的临床标记 OCT 图像上进行了测试。对于每个输入的 OCT 图像，我们的 MIML-LR 方法可以自动识别病变的数量并分配类别标签，对于具有多种病变的情况，平均准确率达到 88.72%，这有助于黄斑疾病的诊断和治疗。
