Ivana Išgum is a distinguished professor in AI and Medical Imaging at the Amsterdam University Medical Centre, Univerity of Amsterdam. Ivana is leading Quantitative Healthcare Analysis (qurAI) group, an interfaculty research group embedded in Faculties of Medicine and Science. Her group is focusing on the development of algorithms for quantitative analysis of medical images to enable automatic patient risk profiling, diagnosis and prognosis using AI techniques. In the development, she is also focusing on wide-range of issues regarding implementation of AI software in healthcare, such as ethical and legal aspects.
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In 100 low-dose non-contrast enhanced non-ECG synchronized screening chest CT scans, a reference standard was defined by manually delineating rectangular bounding boxes around three anatomical ROIs — heart, aortic arch, and descending aorta. Every anatomical ROI was automatically identified using a combination of three CNNs, each analyzing one orthogonal image plane. While single CNNs predicted presence or absence of a specific ROI in the given plane, the combination of their results provided a 3D bounding box around it.
Classification performance of each CNN, expressed in area under the receiver operating characteristic curve, was ≥0.988. Additionally, the performance of ROI localization was evaluated. Median Dice scores for automatically determined bounding boxes around the heart, aortic arch, and descending aorta were 0.89, 0.70, and 0.85 respectively. The results demonstrate that accurate automatic 3D localization of anatomical structures by CNN-based 2D image classification is feasible.
Evaluation of an automatic brain segmentation method developed for neonates on adult MR brain images
For this study, a set of 19 preterm - but otherwise healthy - infants scanned coronally with 3T MRI at the postmenstrual age of 30 weeks were selected. In ten patients (test set), the gray and white matter were manually annotated by an expert on the T2-weighted scans. Manual segmentations were used to extract cortical volume, surface area, thickness, and curvature using voxel-based methods. To compute these biomarkers per region in every patient, a template brain image has been generated by iterative registration and averaging of the scans of the remaining nine patients. This template has been manually divided in eight regions, and is transformed to every test image using elastic registration.
In the results, gray and white matter volumes and cortical surface area appear symmetric between hemispheres, but small regional differences are visible. Cortical thickness seems slightly higher in the right parietal lobe than in other regions. The parietal lobes exhibit a higher global curvature, indicating more complex folding compared to other regions.
The proposed approach can potentially - together with an automatic segmentation algorithm - be applied as a tool to assist in early diagnosis of abnormalities and prediction of the development of the cognitive abilities of these children.
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