Precise segmentation of rectal cancer tumors on routine MRI is critical for accurate clinical staging and downstream computational analyses. While deep learning-based segmentation algorithms have shown much promise in automating the otherwise tedious, subjective, and costly process of manual segmentation, they require significant amounts of manually annotated data for training. To address these limitations of deep learning-based segmentation models, we present a novel deep learning framework that incorporates human-in-the-loop (HITL) refinement to automatically delineate rectal tumors on multi-plane pre-treatment MR imaging. When evaluated on multiple holdout validation cohorts including a clinical trial dataset, the post-HITL segmentation model significantly outperformed the pre-HITL model with median dice similarity coefficient of 0.763 and Hausdorff distance of 28.4mm in comparison to 0.601 and 31.8mm, respectively. HITL refinement learning also significantly accelerated the manual annotation process by 20 minutes. HITL learning represents a feasible, effective, and efficient solution to semi-automated tumor segmentation on routine rectal cancer MRI scans.
KEYWORDS: Magnetic resonance imaging, Signal to noise ratio, Motion models, Tissues, Convex optimization, Reconstruction algorithms, Image filtering, Data modeling
In many applications based on kinetic evaluation analysis and model fitting, quantitative mapping retrieved on data series from modalites such as MRI is completed on a voxel-by-voxel basis, where motion and low signal to noise ratio (SNR) would considerably degenerate the reliability of estimations. The coherence of image series in space and time can be used as prior knowledge to mitigate this occurrence. In this study, spatial and temporal higher-order total variations (HOTVs) are applied on a data series of MRI signal (e.g. dynamic contrast-enhanced (DCE) MRI and intravoxel incoherent motion (IVIM) MRI) to exploit the coherence of signal in space and time to minimize the variabilities caused by motion as well as improving quality of images with low SNR while retaining the physical details of original data properly. Simultaneously applying spatial and temporal HOTVs on images is non-trivial in implementation since it is a non-smooth optimization problem with multiple regularizers. Therefore, we use the proximal gradient method as well as a primal-dual split proximal mechanism to address the problem properly. In addition to increase the reliability of quantitative parametric map estimations, this preprocessing procedure can be included into many existing map estimation algorithms and pipelines effortlessly. We demonstrate our method on the parametric maps estimation for DCE MRI and IVIM MRI.
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