Robust Cell Detection of Histopathological Brain Tumor Images Using Sparse Reconstruction and Adaptive Dictionary Selection

• Published in: IEEE transactions on medical imaging Vol. 35; no. 6; pp. 1575 - 1586
• Main Authors: Su, Hai, Xing, Fuyong, Yang, Lin
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.06.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Automation; Brain; Brain - cytology; Brain - diagnostic imaging; Brain Neoplasms - diagnostic imaging; Cell detection; Diagnosis; Dictionaries; Handles; Histocytochemistry; Humans; Image Interpretation, Computer-Assisted - methods; Image reconstruction; Machine Learning; Medical services; Reconstruction; Robustness; Shape; sparse reconstruction; Testing; Transforms; trivial templates; Tumors
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2016.2520502

Successful diagnostic and prognostic stratification, treatment outcome prediction, and therapy planning depend on reproducible and accurate pathology analysis. Computer aided diagnosis (CAD) is a useful tool to help doctors make better decisions in cancer diagnosis and treatment. Accurate cell detection is often an essential prerequisite for subsequent cellular analysis. The major challenge of robust brain tumor nuclei/cell detection is to handle significant variations in cell appearance and to split touching cells. In this paper, we present an automatic cell detection framework using sparse reconstruction and adaptive dictionary learning. The main contributions of our method are: 1) A sparse reconstruction based approach to split touching cells; 2) An adaptive dictionary learning method used to handle cell appearance variations. The proposed method has been extensively tested on a data set with more than 2000 cells extracted from 32 whole slide scanned images. The automatic cell detection results are compared with the manually annotated ground truth and other state-of-the-art cell detection algorithms. The proposed method achieves the best cell detection accuracy with a F 1 score = 0.96.