Multi-Modality Breast MRI Segmentation Using nn-UNet for Preoperative Planning of Robotic Surgery Navigation

Segmentation of the chest region and breast tissues is essential for surgery planning and navigation. This paper proposes the foundation for preoperative segmentation based on two cascaded architectures of deep neural networks (DNN) based on the state-of-the-art nnU-Net. Additionally, this study introduces a polyvinyl alcohol cryogel (PVA-C) breast phantom based on the segmentation of the DNN automated approach, enabling the experiments of navigation system for robotic breast surgery. Multi-modality breast MRI datasets of T2W and STIR images were acquired from 10 patients. Segmentation evaluation utilized the Dice Similarity Coefficient (DSC), segmentation accuracy, sensitivity, and specificity. First, a single class labeling was used to segment the breast region. Then it was employed as an input for three-class labeling to segment fat, fibroglandular (FGT) tissues, and tumorous lesions. The first architecture has a 0.95 DCS, while the second has a 0.95, 0.83, and 0.41 for fat, FGT, and tumor classes, respectively

Bilateral hyperdense middle cerebral arteries: Stroke sign or not?

Hyperdense middle cerebral artery (MCA) is a classic sign of acute thromboembolic disease. Simultaneous bilateral occurrence is uncommon and traditionally attributed to physiological hemoconcentration or attributable to imaging artifact. We present the case of a 71-year-old man whose admission noncontrast computed tomography (CT) demonstrated bilateral hyperdense middle cerebral arteries without other radiographic evidence of acute stroke. CT angiography confirmed bilateral MCA, M1 segment vascular occlusion and follow-up noncontrast CT demonstrated MCA territory infarctions. Keyword: Bilateral hyperdense middle cerebral arterie