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Identifying, measuring and reporting lesions accurately and comprehensivelyfrom patient CT scans are important yet time-consuming procedures forphysicians. Computer-aided lesion/significant-findings detection techniques areat the core of medical imaging, which remain very challenging due to thetremendously large variability of lesion appearance, location and sizedistributions in 3D imaging. In this work, we propose a novel deep anchor-freeone-stage VULD framework that incorporates (1) P3DC operators to recycle thearchitectural configurations and pre-trained weights from the off-the-shelf 2Dnetworks, especially ones with large capacities to cope with data variance, and(2) a new SPR method to effectively regress the 3D lesion spatial extents bypinpointing their representative key points on lesion surfaces. Experimentalvalidations are first conducted on the public large-scale NIH DeepLesiondataset where our proposed method delivers new state-of-the-art quantitativeperformance. We also test VULD on our in-house dataset for liver tumordetection. VULD generalizes well in both large-scale and small-sized tumordatasets in CT imaging.

Deep Volumetric Universal Lesion Detection using Light-Weight Pseudo 3D Convolution and Surface Point Regression