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In thoracoabdominal aortic aneurysm repair, repairing the visceral and segmental arteries is challenging. Although there is a pre-hand-sewn and multi-branched graft based on the conventional image-based technique, it has shortcomings in precisely positioning and directing the visceral and segmental arteries. Here, we introduce two new reconstruction techniques using patient-specific 3D-printed graft reconstruction guides: (1) model-based technique that presents the projected aortic graft, visualizing the main aortic body and its major branches and (2) guide-based technique in which the branching vessels in the visualization model are replaced by marking points identifiable by tactile sense. We demonstrate the effectiveness by evaluating conventional and new techniques based on accuracy, marking time requirement, reproducibility, and results of survey to surgeons on the perceived efficiency and efficacy. The graft reconstruction guides cover the segmentation, design, fabrication, post-processing, and clinical application of open surgical repair of thoracoabdominal aneurysm, and proved to be efficient for accurately reconstructing customized grafts. [ABSTRACT FROM AUTHOR]

We quantitatively evaluated breast tumor movement and volume changes between magnetic resonance imaging (MRI) scans in prone and supine positions. Twenty-seven breast tumor patients who received neoadjuvant systemic therapy (NST) for breast-conserving surgery were studied. Before and after NST, MRI scans in prone and supine positions were performed immediately. Tumor segmentation, volume, and position of tumors were evaluated in both positions. Average tumor volumes in prone and supine positions did not significantly differ (p = 0.877). Tumor movement from prone to supine positions from the origin of the bottom center of the sternum was strongly correlated with the distance from the tumor center to the chest wall (r = 0.669; p < 0.05). Tumor changes from prone to supine positions measured from the origin of the nipple depended on the location of the tumor in the breast. The prone-to-supine movement of all tumors from the origin of the bottom center of the sternum tended to move outward from the sagittal centerline of the body on the coronal plane, to the inside of the body on the sagittal plane, and outward and downward close to the body on the axial plane, which might help in planning operations using prone MRI in supine-position breast cancer surgery. [ABSTRACT FROM AUTHOR]

Segmentation is fundamental to medical image analysis. Recent advances in fully convolutional networks has enabled automatic segmentation; however, high labeling efforts and difficulty in acquiring sufficient and high-quality training data is still a challenge. In this study, a cascaded 3D U-Net with active learning to increase training efficiency with exceedingly limited data and reduce labeling efforts is proposed. Abdominal computed tomography images of 50 kidneys were used for training. In stage I, 20 kidneys with renal cell carcinoma and four substructures were used for training by manually labelling ground truths. In stage II, 20 kidneys from the previous stage and 20 newly added kidneys were used with convolutional neural net (CNN)-corrected labelling for the newly added data. Similarly, in stage III, 50 kidneys were used. The Dice similarity coefficient was increased with the completion of each stage, and shows superior performance when compared with a recent segmentation network based on 3D U-Net. The labeling time for CNN-corrected segmentation was reduced by more than half compared to that in manual segmentation. Active learning was therefore concluded to be capable of reducing labeling efforts through CNN-corrected segmentation and increase training efficiency by iterative learning with limited data. [ABSTRACT FROM AUTHOR]

These authors contributed equally: Taehun Kim, Kyung Hwa Lee and Sungwon Ham Correction to: I Scientific Reports i https://doi.org/10.1038/s41598-019-57242-9, published online 15 January 2020 The original version of this Article contained an error in the spelling of the author Kyung Hwa Lee which was incorrectly given as Kyunghwa Lee. The original Article has been corrected. [Extracted from the article]