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Multi‐domain features for reducing false positives in automated detection of clustered microcalcifications in digital breast tomosynthesis
Author(s) -
Zhang Fan,
Wu Shandong,
Zhang Cheng,
Chen Qian,
Yang Xiaodong,
Jiang Ke,
Zheng Jian
Publication year - 2019
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.13394
Subject(s) - tomosynthesis , false positive paradox , artificial intelligence , computer science , microcalcification , focus (optics) , pattern recognition (psychology) , voxel , computer aided diagnosis , computer vision , linear discriminant analysis , mammography , medicine , physics , cancer , breast cancer , optics
Purpose In digital breast tomosynthesis ( DBT ) imaging, a microcalcification ( MC ) cluster may span across different slices and blurring exists in the out‐of‐focus slices. We developed a radiomics approach to extract features from focus slice and combine multiple spatial domains to reduce false positives ( FP s) in an automated pipeline of detecting MC clusters. Methods We performed a retrospective study on a cohort of 290 Chinese women patients with a total of 580 DBT volumes. We developed an automated MC detection pipeline that consists of two stages: an initial detection to identify a set of MC candidates that may include many FP s, followed by a radiomics‐based classification model to identify and reduce the FP s. We extract both two‐dimensional (2D) and three‐dimensional (3D) radiomics features from multiple spatial domains, including a focus slice, projection image, and tomographic volume. A linear discriminant classifier was used coupled with a sequential forward feature selection procedure. The free‐response operating characteristics ( FROC ) curve and partial area under the FROC curve ( pAUC ) in the FP rate range of 0 to 2 per DBT volume were used to evaluate the model's performance. Results At a sensitivity of 90%, the FP rate was reduced from 1.3 to 0.2 per DBT volume after applying the multi‐domain‐based classification on the initial detections. The multi‐domain yielded a significantly higher pAUC compared to the initial detection (increase of pAUC = 0.2278, P < 0.0001), focus slice (increase of pAUC = 0.0345, P = 0.0152), project image (increase of pAUC = 0.1043, P < 0.0001), and tomographic volume (increase of pAUC = 0.0791, P = 0.0032). Conclusion The radiomic features extracted from the three domains may provide complementary information and their integration can significantly reduce FP s in automated detection of MC s in DBT volumes on a large Chinese women population.