基于加权损失函数的粘连白细胞分割算法

针对粘连白细胞很难精准分割的问题, 提出一种基于深度学习的粘连白细胞分割算法. 首先, 将急性淋巴细胞白血病患者的血液细胞显微图像的色彩空间由RGB转换至HSV, 滤除红细胞并提取白细胞； 其次, 对提取结果中的粘连白细胞, 将细胞边界设定为除前景和背景外的第三类, 在深度学习分割模型训练过程中引入基于类别权重的加权交叉熵损失函数, 使模型学习到更多的细胞边界特征. 实验结果表明, 用该方法分割数据集ALL_IDB1中的白细胞, 准确率达95.19%.

Adhesive Leukocyte Segmentation Algorithm Based on Weighted Loss Function

Aiming at the problem that it was difficult to segment adhesive leukocyte accurately, we proposed an adhesive leukocytes segmentation algorithm based on deep learning. Firstly, the color space of the blood cell microscopic images of patients with acute lymphoblastic leukemia was transformed from RGB to HSV, in order to filter out red blood cells and extract leukocytes. Secondly, for the adhesive leukocytes in extraction results, the cell border was set as the third class, in addition to foreground and background. During the training process of deep learning segmentation model, a weighted cross-entropy loss function based on class weight was introduced to make the model learn more cell border features. The experimental results show that using the proposed method to segment the leukocytes in the dataset ALL_IDB1 can achieve an accuracy of 95.19%.