There are few algorithms for segmenting cryosection brain images, and most existing segmentation techniques presented limited precision and low efficiency. To address these problems, this paper proposed a novel deep feature learning-based segmentation algorithm using sparse autoencoder (SAE). At the stage of feature ex-traction, SAE is trained twice to enhance the discriminability of the deep-learned feature representations. At the stage of classification, a softmax classifier is used for segmenting different objects. Experimental results of white matter segmentation on the Chinese Visible Human (CVH) dataset and its 3-D reconstruction show that, the learned deep feature performs much better in discriminability compared with other representative hand-crafted features (such as intensity, histogram of oriented gradient and principal components analysis) and achieves higher recognition accuracy.%针对目前基于数字人脑切片图像的分割算法较少，分割精度和有效性较低等不足，提出一种基于稀疏自编码器(SAE)深度特征学习的分割算法。在特征提取阶段，采用从粗到精两级方式对 SAE 进行训练，以增强模型学习到的深度特征的鉴别能力；在分类阶段，使用 softmax 分类器进行目标分割。对中国可视化人体(CVH)数据集的脑白质分割及三维重建的实验结果表明，相对于其他传统的手工特征(如图像强度特征、方向梯度直方图特征和主成分分析特征)， SAE 提取的图像深度特征具有更强的鉴别能力，显著地提高了分割精度。
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