Image Synthesis-based Late Stage Cancer Augmentation and Semi-Supervised Segmentation for MRI Rectal Cancer Staging

Rectal cancer is one of the most common diseases and a major cause of mortality. For deciding rectal cancer treatment plans, T-staging is important. However, evaluating the index from preoperative MRI images requires high radiologists' skill and experience. Therefore, the aim of this study is to segment the mesorectum, rectum, and rectal cancer region so that the system can predict T-stage from segmentation results. Generally, shortage of large and diverse dataset and high quality annotation are known to be the bottlenecks in computer aided diagnostics development. Regarding rectal cancer, advanced cancer images are very rare, and per-pixel annotation requires high radiologists' skill and time. Therefore, it is not feasible to collect comprehensive disease patterns in a training dataset. To tackle this, we propose two kinds of approaches of image synthesis-based late stage cancer augmentation and semi-supervised learning which is designed for T-stage prediction. In the image synthesis data augmentation approach, we generated advanced cancer images from labels. The real cancer labels were deformed to resemble advanced cancer labels by artificial cancer progress simulation. Next, we introduce a T-staging loss which enables us to train segmentation models from per-image T-stage labels. The loss works to keep inclusion/invasion relationships between rectum and cancer region consistent to the ground truth T-stage. The verification tests show that the proposed method obtains the best sensitivity (0.76) and specificity (0.80) in distinguishing between over T3 stage and underT2. In the ablation studies, our semi-supervised learning approach with the T-staging loss improved specificity by 0.13. Adding the image synthesis-based data augmentation improved the DICE score of invasion cancer area by 0.08 from baseline.Comment: 10 pages, 7 figures, Accepted to Data Augmentation, Labeling, and Imperfections (DALI) at MICCAI 202

Cardiac diastolic dysfunction predicts in-hospital mortality in acute ischemic stroke with atrial fibrillation

Background: The aim of this study was to identify whether diastolic dysfunction predicts in-hospital death in ischemic stroke patients with atrial fibrillation. Method: We retrospectively analyzed data fromenrolled patients with ischemic stroke patients with atrial fibrillation who presented within 24 h of onset. All patients underwent transthoracic echocardiography to evaluate diastolic filling pressure estimated as the ratio of early transmitral flow velocity (E) to mitral annular velocity (e\u27)within 24 h of admission.Weevaluated initial ischemic lesion volume andNational Institute of Health Stroke Scale (NIHSS) score. Results: Two hundred and sixty-six patients were enrolled.During hospitalization, 30 patients (11%) died. The deceased group had a higher NIHSS score, a higher D-dimer level, a higher creatinine level, a larger initial ischemic lesion volumeand a higher E/e\u27 ratio than those in the survival group. In amultivariate analysis, a higher E/e\u27 ratio was an independent predictor of in-hospital death. The cutoff value for the E/e\u27 ratio for prediction in-hospital death was 20 with the sensitivity of 75% and specificity of 86%. Conclusion: Diastolic dysfunction may be associatedwith in-hospital death in ischemic stroke patientswith atrial fibrillation

Measurement of Carotid Stenosis Using Duplex Ultrasonography with a Microconvex Array Transducer: A Validation with Cerebral Angiography

Background: We aimed to evaluate the validity of duplex ultrasonography (DUS) using a microconvex array transducer (MAT) with enhanced flow imaging (EFI) for visualization of the distal, internal carotid artery (ICA) and the accurate assessment of ICA stenosis. Methods: Patients who underwent both DUS and digital subtraction angiography (DSA) were registered for this study. DUS was performed by using a linear array transducer (LAT) and an MAT with EFI. The visibility of the ICA was compared between the 2 transducers. ICA stenosis was evaluated by the North American Symptomatic Carotid Endarterectomy Trial (NASCET) method on DUS, and the peak systolic flow velocity (PSV) was evaluated by using an MAT. These results were compared with DSA. Results: In 238 internal carotid arteries, the average length of visualized ICA was longer for DUS using an MAT than an LAT (38.7 ± 11.7 mm versus 25.8 ± 9.8 mm, P <.0001). In 68 stenotic, internal carotid arteries, the degree of ICA stenosis detected by the NASCET method on DUS was correlated to that on DSA (P <.0001, r =.969, and adjusted r2 =.938). PSV also correlated to NASCET method on DSA (P <.0001, r =.804, and adjusted r2 =.640). Conclusions: DUS using an MAT with EFI technology could reveal more extended distal views of the ICA and was strongly correlated with NASCET method on DSA