Notice of Violation of IEEE Publication Principles: Deep Learning Assisted Image Interactive Framework for Brain Image Segmentation

Notice of Violation of IEEE Publication Principles Learning Assisted Image Framework for Brain Image Segmentation, by Y. Han and Z. Zhang, in IEEE Access, vol. 8, June 2020, pp. 117028-117035 After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles. This paper contains significant portions of text from the papers cited below that were paraphrased without attribution. “DeepIGeoS: A Deep Geodesic Framework for Medical Image Segmentation, by Guotai Wang, Maria A. Zuluaga, Wenqi Li, Rosalind Pratt, Premal A. Patel, Michael Aertsen, Tom Doel, Anna L. David, Jan Deprest, Sebastien Ourselin, and Tom Vercauteren in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 41, no. 7, July 2019, pp. 1559-1572 Interactive Medical Image Segmentation Using Deep Learning With Image-Specific Fine Tuning, by Guotai Wang, Wenqi Li, Maria A. Zuluaga, Rosalind Pratt, Premal A. Patel, Michael Aertsen, Tom Doel, Anna L. David, Jan Deprest, Sebastien Ourselin, and Tom Vercauteren in IEEE Transactions on Medical Imaging, vol. 37, no. 7, July 2018, pp. 1562-1573, Exacting medical imaging, surgical planning, and many others are very important to handle brain image segmentation. The Convolutional Neural Networks (CNN) has been developed by the efficient auto segmentation technology. In fact, the clinical outcomes are not appropriately specific and detailed. Nevertheless, the lack of sensitivity to images and lack of generality is reduced in traditionally invisible object classes. In this paper, Deep Learning Assisted Image Medical Image Segmentation (DL-IIMIS) is proposed to tackle these difficulties by including CNNs in the bounding box and scribble-based pipeline. To adapt a CNN model to one test frame, it is proposed that image fine tuning and geodesic transformations can be either unsupervised or supervised. In this frame, two applications are involved: 2-D multi-organ magnetic resonance (MR) segmentation, with only two types of training and 3-D segmentation within brain tumor center and in entire brain tumors with different MR sequences where only one MR sequence is reported. Compared with other algorithms, the proposed framework can output a better performance in brain image segmentation.