Prostate lesion delineation from multiparametric magnetic resonance imaging based on locality alignment discriminant analysis

• Published in: Medical physics (Lancaster) 2018-10, Vol.45 (10), p.4607-4618
• Main Authors: Lin, Mingquan, Chen, Weifu, Zhao, Mingbo, Gibson, Eli, Bastian‐Jordan, Matthew, Cool, Derek W., Kassam, Zahra, Liang, Huageng, Chow, Tommy W.S., Ward, Aaron D., Chiu, Bernard
• Format: Article
• Language: English
• Subjects: Algorithms; cancer lesion delineation; Discriminant Analysis; Humans; Image Processing, Computer-Assisted - methods; Linear Models; locality alignment discriminant analysis (LADA); Magnetic Resonance Imaging; Male; multiparametric MRI (mpMRI); prostate cancer; Prostatic Neoplasms - diagnostic imaging
• ISSN: 0094-2405; 2473-4209
• DOI: 10.1002/mp.13155

Purpose Multiparametric MRI (mpMRI) has shown promise in the detection and localization of prostate cancer foci. Although techniques have been previously introduced to delineate lesions from mpMRI, these techniques were evaluated in datasets with T2 maps available. The generation of T2 map is not included in the clinical prostate mpMRI consensus guidelines; the acquisition of which requires repeated T2‐weighted (T2W) scans and would significantly lengthen the scan time currently required for the clinically recommended acquisition protocol, which includes T2W, diffusion‐weighted (DW), and dynamic contrast‐enhanced (DCE) imaging. The goal of this study is to develop and evaluate an algorithm that provides pixel‐accurate lesion delineation from images acquired based on the clinical protocol. Methods Twenty‐five pixel‐based features were extracted from the T2‐weighted (T2W), apparent diffusion coefficient (ADC), and dynamic contrast‐enhanced (DCE) images. The pixel‐wise classification was performed on the reduced space generated by locality alignment discriminant analysis (LADA), a version of linear discriminant analysis (LDA) localized to patches in the feature space. Postprocessing procedures, including the removal of isolated points identified and filling of holes inside detected regions, were performed to improve delineation accuracy. The segmentation result was evaluated against the lesions manually delineated by four expert observers according to the Prostate Imaging‐Reporting and Data System (PI‐RADS) detection guideline. Results The LADA‐based classifier (60 ± 11%) achieved a higher sensitivity than the LDA‐based classifier (51 ± 10%), thereby demonstrating, for the first time, that higher classification performance was attained on the reduced space generated by LADA than by LDA. Further sensitivity improvement (75 ± 14%) was obtained after postprocessing, approaching the sensitivities attained by previous mpMRI lesion delineation studies in which nonclinical T2 maps were available. Conclusion The proposed algorithm delineated lesions accurately and efficiently from images acquired following the clinical protocol. The development of this framework may potentially accelerate the clinical uses of mpMRI in prostate cancer diagnosis and treatment planning.