  Identifying, measuring and reporting lesions accurately and comprehensively
from patient CT scans are important yet time-consuming procedures for
physicians. Computer-aided lesion/significant-findings detection techniques are
at the core of medical imaging, which remain very challenging due to the
tremendously large variability of lesion appearance, location and size
distributions in 3D imaging. In this work, we propose a novel deep anchor-free
one-stage VULD framework that incorporates (1) P3DC operators to recycle the
architectural configurations and pre-trained weights from the off-the-shelf 2D
networks, especially ones with large capacities to cope with data variance, and
(2) a new SPR method to effectively regress the 3D lesion spatial extents by
pinpointing their representative key points on lesion surfaces. Experimental
validations are first conducted on the public large-scale NIH DeepLesion
dataset where our proposed method delivers new state-of-the-art quantitative
performance. We also test VULD on our in-house dataset for liver tumor
detection. VULD generalizes well in both large-scale and small-sized tumor
datasets in CT imaging.
