Kidneys are most easily segmented by convolutional neural networks (CNN) on contrast enhanced CT (CECT) images, but their segmentation accuracy may be reduced when only non-contrast CT (NCCT) images are available. The purpose of this work was to investigate the improvement in segmentation accuracy when implementing a generative adversarial network (GAN) to create virtual contrast enhanced (vCECT) images from non-contrast inputs. A 2D cycleGAN model, incorporating an additional idempotent loss function to restrict the GAN from making unnecessary modifications to data already in the translated domain, was trained to generate virtual contrast enhanced images on 286 paired non-contrast and contrast enhanced inputs. A 3D CNN trained on contrast enhanced images was applied to segment the kidneys in a test set of 20 paired non-contrast and contrast enhanced images. The non-contrast images were converted to virtual contrast enhanced images, then kidneys in both image conditions were segmented by the CNN. Segmentation results were compared to analyst annotations on non-contrast images visually and by Dice Coefficient (DC). Segmentation on virtual contrast enhanced images were more complete with fewer extraneous detections compared to non-contrast images in 16/20 cases. Mean(±SD) DC was 0.88(±0.80), 0.90(±0.03), and 0.95(±0.05) for non-contrast, virtual contrast enhanced, and real contrast enhanced, respectively. Virtual contrast enhancement visually improved segmentation quality, poor performing cases had their performance improved resulting in an overall reduction in DC variation, and the minimum DC increased from 0.65 to 0.85. This work provides preliminary results demonstrating the potential effectiveness of using a GAN for virtual contrast enhancement to improve CNN-based kidney segmentation on non-contrast images.
Purpose: Convolutional Neural Networks (CNN) are frequently used for organ segmentation in medical imaging. Many CNNs, however, struggle for acceptance into clinical practice because of mis-segmentations that are obvious to radiologists or other experts. We propose using a Cognitive AI framework that applies anatomical knowledge and machine reasoning to check and improve CNN segmentations and avoid obvious mis-segmentations.Methods: We used the open-source SimpleMind framework that allows post-processing and machine reasoning to be applied to CNN segmentation results. Within this framework, a 3D-UNet CNN was trained on 212 contrast-enhanced kidney CT scans. From an anatomical knowledge base, SimpleMind derived the following Cognitive AI post-processing steps: (1) identification of the abdomen, (2) segmentation of the spine by searching for bone in the posterior region of the abdomen, (3) CNN output separation into right and left kidneys using the spine as reference, (4) refinement of individual kidney segmentations using thresholds of volume, HU, and voxel count, which are designed to eliminate disconnected false positives, (5) morphological opening to reduce connected false positives bordering the kidneys' segmentations. On a test set of 53 scans, using reference annotations, Dice Coefficient (DC), Hausdorff Distance (HD) and Average Symmetric Surface Distances (ASSD) were computed and compared for the CNN and post-processed outputs.Results: Post-processing with Cognitive AI reduced the kidney segmentation HD from 46.4±55.8 mm to 30.7±17.6 mm, with the decrease in variance being statistically significant (p = 0.0296). DC and ASSD metrics were also improved.Conclusions: This initial work demonstrates that the incorporation of anatomical knowledge using Cognitive AI techniques can improve the segmentation accuracy of CNNs. The CNN provides very good overall kidney segmentation performance, and in cases where segmentation errors occur the post processing was able to improve performance.
This study investigates the effect of radiation dose reduction of a renal perfusion CT protocol on quantitative imaging features for patients of different sizes. Our findings indicate that the impact of dose reduction is significantly different between patients of different sizes for standard deviation, entropy, and GLCM joint average at all dose levels evaluated, and for mean at the lowest dose level evaluated (p < .001). These results suggest that a size-based scanning protocol may be needed to provide quantitative results that are robust with respect to patient size.
e13545 Background: Automating RECIST 1.1 would offer benefits in speed, consistency, and cost of clinical trial imaging reads. Detection of new lesions is subjective and a common source of reader adjudication. Current AI lesion detection typically uses single timepoint images. The use of temporal data in AI is a rapidly evolving field in non-medical applications and new lesion detection in response assessment may benefit from longitudinal multi timepoint images. We explore the impact of longitudinal image input to deep neural networks for new lesion detection; as a proof of concept, we compare single vs dual timepoint input in new liver lesion detection in CT. Methods: We utilized CT images from two public datasets: 1) diseased liver (DL), a subset of DeepLesion (doi: 10.1117/1.JMI.5.3.036501), and 2) healthy liver (HL) from potential liver donors (Med Image Anal 69 (2021) 101950).. Due to the lack of a publicly available longitudinal image dataset with new lesions, we simulated (“sim”) a longitudinal study by registering HL scans (sim healthy baseline) with DL scans (sim follow up with new lesion) and selecting well-paired images using mutual information. From each pair of scans, we select one positive (with new lesion on follow up) and one negative (without lesion on follow up) 6x6cm image patch from within the liver, creating a dataset of 3296 patches with a balanced number of pos/neg samples. We trained two deep neural networks to classify the presence of a new liver lesion: 1) a conventional single timepoint model (ST) trained on DL patches only, i.e. using the follow up scan only, and 2) an experimental dual-timepoint model (DT) trained on patch pairs from DL-HL paired scans. We used 5-fold cross validation and paired t tests to compare model AUC and accuracy. Results: Of 10,594 CT studies (4,427 patients, pts) in DeepLesion, we paired 1648 DL scans (946 pts with liver lesions) with one of 20 registered HL scans (20 pts). The DT model outperformed the ST model, increasing AUC from 0.914 ± 0.002 to 0.929 ± 0.005 (p=0.040), with similar trends in accuracy (dataset split, Table 1). Conclusions: The DT model outperformed the ST model in the detection of new liver lesions, suggesting preliminary the benefit of multi-timepoint input to a deep neural network using sim longitudinal data. The use of longitudinal image data to improve interval change detection is a step towards automating radiologic response assessment in clinical trials. We plan to extend this work to automate new lesion detection in real clinical trial longitudinal images. Results (bold) are reported on test set across 5 folds. N image patches (ST) or patch pairs (DT). [Table: see text]
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