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Effective and non-invasive radiological imaging based tumor/lesion characterization (e.g., subtype classification) has long been a major aim in the oncology diagnosis and treatment procedures, with the hope of reducing needs for invasive surgical biopsies. Prior work are generally very restricted to a limited patient sample size, especially using patient studies with confirmed pathological reports as ground truth. In this work, we curate a patient cohort of 1305 dynamic contrast CT studies (i.e., 5220 multi-phase 3D volumes) with pathology confirmed ground truth. A novel fully-automated and multi-stage liver tumor characterization framework is proposed, comprising four steps of tumor proposal detection, tumor harvesting, primary tumor site selection, and deep texture-based characterization. More specifically, (1) we propose a 3D non-isotropic anchor-free lesion detection method; (2) we present and validate the use of multi-phase deep texture learning for precise liver lesion tissue characterization, named spatially adaptive deep texture (SaDT);(3) we leverage small-sized public datasets to semi-automatically curate our large-scale clinical dataset of 1305 patients where four main liver tumor subtypes of primary, secondary, metastasized and benign are presented. Extensive evaluations demonstrate that our new data curation strategy, combined with the SaDT deep dynamic texture analysis, can effectively improve the mean F1 scores by > 8.6% compared with baselines, in differentiating four major liver lesion types. This is a significant step towards the clinical goal.

3D delineation of anatomical structures is a cardinal goal in medical imaging analysis. Prior to deep learning, statistical shape models (SSMs) that imposed anatomical constraints and produced high quality surfaces were a core technology. Today’s fully-convolutional networks (FCNs), while dominant, do not offer these capabilities. We present deep implicit statistical shape models (DISSMs), a new approach that marries the representation power of deep networks with the benefits of SSMs. DISSMs use an implicit representation to produce compact and descriptive deep surface embeddings that permit statistical models of anatomical variance. To reliably fit anatomically plausible shapes to an image, we introduce a novel rigid and non-rigid pose estimation pipeline that is modelled as a Markov decision process (MDP). Intra-dataset experiments on the task of pathological liver segmentation demonstrate that DISSMs can perform more robustly than four leading FCN models, including nnU-Net + an adversarial prior: reducing the mean Hausdorff distance (HD) by 7.5-14.3 mm and improving the worst case Dice-Sørensen coefficient (DSC) by 1.2-2.3%. More critically, cross-dataset experiments on an external and highly challenging clinical dataset demonstrate that DISSMs improve the mean DSC and HD by 2.1-5.9% and 9.9-24.5 mm, respectively, and the worst-case DSC by 5.4-7.3%. Supplemental validation on a highly challenging and low-contrast larynx dataset further demonstrate DISSM’s improvements. These improvements are over and above any benefits from representing delineations with high-quality surfaces.