Dr. Stuart Inglis Profile

Dr. Stuart Inglis

at Univ at Buffalo

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Proceedings Article | 15 February 2021 Poster + Presentation + Paper

Quantifying the importance of spatial anatomical context in cadaveric, non-contrast enhanced organ segmentation

Steven Lewis, Stuart Inglis, Scott Doyle

Proceedings Volume 11595, 115952J (2021) https://doi.org/10.1117/12.2581140

KEYWORDS: Image segmentation, 3D modeling, 3D image processing, Statistical modeling, Kidney, Radiology, Medical imaging, Image compression, Convolution, Computed tomography

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Volumetric segmentation using deep learning is a computationally expensive task, but one with great utility for medical image analysis in radiology. Deep learning uses the process of convolution to calculate voxel level relationships and predict class membership of each voxel (e.g. segmentation). We hypothesize that (1) kidney segmentation in cadaveric, non-contrast enhanced CT images is possible; (2) a volumetric UNet (VNet) architecture will out-perform a 2D UNet architecture in kidney segmentation; and (3) as increasing anatomically relevant information present within the volumes will increase the ability of the system to understand the relationship of anatomical structures, thus enabling more accurate segmentation. In this project we utilized a difficult dataset (cadaveric, non-contrast enhanced CT data) to determine how much anatomical information is necessary to obtain a quantifiable segmentation with the lowest Hausdorff Distance and highest Dice Coefficient values between the output and the ground truth mesh. We used a 70/20/10% training testing and validation split with a total N of 30 specimens. In order to test the anatomical context required to properly segment structures we evaluated and compared the performance of four separate segmentation models: (1) a 2D UNet model that pulled random cross sections from the volumes for training; (2) a 2D UNet model that had the training samples augmented with 3D perturbations for more anatomical context; (3) a 3D VNet with volumetric patching and a padded border to protect against edge artifacts; and (4) a 3D VNet with volumetric patching and image compression by ½ the volume with the padded border. Our results show that as anatomical context in the image or volume increases, segmentation performance also improves.

Proceedings Article | 16 March 2020 Paper

Quantitative evaluation of cadaveric contrast agents: identifying anatomical structures with BriteVu

Steven Lewis, Stuart Inglis, Bruce Wainman, Scott Doyle

Proceedings Volume 11312, 1131231 (2020) https://doi.org/10.1117/12.2549331

KEYWORDS: Kidney, Image segmentation, Computed tomography, Image enhancement, 3D modeling, Image contrast enhancement

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High-contrast Computed Tomography (CT) imaging provides a non-invasive, detailed look at internal anatomy (soft tissues, bone, and cartilage) and is used throughout the clinical, research, and academic anatomical fields. Cadaveric specimens are valuable teaching and research tools, but contrast enhancement (CE) of cadavers for clear CT imaging is challenging. The lack of blood flow and pumping mechanisms produces clotting and a barrier to injection of contrast agents. Recently, cadaver preparation techniques for introducing contrast agents have been developed; however, evaluation of cadaveric contrast agents has been qualitative in nature and does not give enough information to adequately assess its efficacy. In this study, we used both quantitative and qualitative techniques to evaluate a cadaveric contrast agent (BriteVu) and its ability to resolve kidney boundaries, structure, and surrounding tissues. We found that BriteVu was able to enhance the macrostructures of the kidneys, and some substructures could be differentiated but not adequately enough for intricate vasculature modeling. Our approach to evaluation of cadaveric contrast agents provides a structural, quantitative understanding that frames cadaveric contrast agents as a tool for structural learning and research.

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