Clustering-Based Dual Deep Learning Architecture for Detecting Red Blood Cells in Malaria Diagnostic Smears.
Clustering-Based Dual Deep Learning Architecture for Detecting Red Blood Cells in Malaria Diagnostic Smears.
复制标题
基于聚类的双重学习结构，用于检测疟疾诊断涂片中的红细胞。
DOI：
10.1109/jbhi.2020.3034863
复制
发表时间：
2021-05
影响因子：
7.7
通讯作者：
Jaeger S
中科院分区：
文献类型：
--
作者：
Kassim YM;Palaniappan K;Yang F;Poostchi M;Palaniappan N;Maude RJ;Antani S;Jaeger S
计算机辅助算法已成为生物医学应用的支柱，以提高手动分割和注释等重复性任务的准确性和可重复性。我们提出了一种新的管道，用于薄血涂片显微镜图像中的红细胞检测和计数，名为RBCNet，使用双深度学习架构。RBCNet由用于细胞簇或超像素分割的U-Net第一阶段组成，然后是用于检测连接组件簇内的小细胞对象的第二细化阶段Faster R-CNN。RBCNet使用细胞聚类而不是区域建议，其对细胞碎片化具有鲁棒性，对于检测非常大的图像中的小对象或精细尺度形态结构具有高度可扩展性，可以使用非重叠的瓦片进行训练，并且在推理期间自适应于具有低内存占用的细胞簇的规模。我们在孟加拉国获得的193名患者的965张图像中的近20万个标记细胞的人类疟疾涂片存档集合上测试了我们的方法，每个患者贡献5张图像。使用RBCNet的细胞检测准确率高于97。新型的双级联RBCNet架构提供了更准确的细胞检测，因为来自U-Net的前景细胞簇掩码自适应地指导检测阶段，与传统和其他深度学习方法相比，导致了显着更高的真阳性和更低的误报率。RBCNet管道为疟疾自动诊断迈出了关键的一步。
Computer-assisted algorithms have become a mainstay of biomedical applications to improve accuracy and reproducibility of repetitive tasks like manual segmentation and annotation. We propose a novel pipeline for red blood cell detection and counting in thin blood smear microscopy images, named RBCNet, using a dual deep learning architecture. RBCNet consists of a U-Net first stage for cell-cluster or superpixel segmentation, followed by a second refinement stage Faster R-CNN for detecting small cell objects within the connected component clusters. RBCNet uses cell clustering instead of region proposals, which is robust to cell fragmentation, is highly scalable for detecting small objects or fine scale morphological structures in very large images, can be trained using non-overlapping tiles, and during inference is adaptive to the scale of cell-clusters with a low memory footprint. We tested our method on an archived collection of human malaria smears with nearly 200,000 labeled cells across 965 images from 193 patients, acquired in Bangladesh, with each patient contributing five images. Cell detection accuracy using RBCNet was higher than 97. The novel dual cascade RBCNet architecture provides more accurate cell detections because the foreground cell-cluster masks from U-Net adaptively guide the detection stage, resulting in a notably higher true positive and lower false alarm rates, compared to traditional and other deep learning methods. The RBCNet pipeline implements a crucial step towards automated malaria diagnosis.