MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention This is an RSS file. You can use it to subscribe to this data in your favourite RSS reader or to display this data on your own website or blog.

Generating Realistic Brain MRIs via a Conditional Diffusion Probabilistic Model
Med Image Comput Comput Assist Interv. 2023 Oct;14227:14-24. doi: 10.1007/978-3-031-43993-3_2. Epub 2023 Oct 1.ABSTRACTAs acquiring MRIs is expensive, neuroscience studies struggle to attain a sufficient number of them for properly training deep learning models. This challenge could be reduced by MRI synthesis, for which Generative Adversarial Networks (GANs) are popular. GANs, however, are commonly unstable and struggle with creating diverse and high-quality data. A more stable alternative is Diffusion Probabilistic Models (DPMs) with a fine-grained training strategy. To overcome their need for extensive computational res...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - January 3, 2024 Category: Radiology Authors: Wei Peng Ehsan Adeli Tomas Bosschieter Sang Hyun Park Qingyu Zhao Kilian M Pohl Source Type: research

Supervised Deep Learning for Head Motion Correction in PET
Med Image Comput Comput Assist Interv. 2022 Sep;13434:194-203. doi: 10.1007/978-3-031-16440-8_19. Epub 2022 Sep 16.ABSTRACTHead movement is a major limitation in brain positron emission tomography (PET) imaging, which results in image artifacts and quantification errors. Head motion correction plays a critical role in quantitative image analysis and diagnosis of nervous system diseases. However, to date, there is no approach that can track head motion continuously without using an external device. Here, we develop a deep learning-based algorithm to predict rigid motion for brain PET by lever-aging existing dynamic PET scan...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - December 18, 2023 Category: Radiology Authors: Tianyi Zeng Jiazhen Zhang Enette Revilla El éonore V Lieffrig Xi Fang Yihuan Lu John A Onofrey Source Type: research

Atlas-based Semantic Segmentation of Prostate Zones
Med Image Comput Comput Assist Interv. 2022 Sep;13435:570-579. doi: 10.1007/978-3-031-16443-9_55. Epub 2022 Sep 16.ABSTRACTSegmentation of the prostate into specific anatomical zones is important for radiological assessment of prostate cancer in magnetic resonance imaging (MRI). Of particular interest is segmenting the prostate into two regions of interest: the central gland (CG) and peripheral zone (PZ). In this paper, we propose to integrate an anatomical atlas of prostate zone shape into a deep learning semantic segmentation framework to segment the CG and PZ in T2-weighted MRI. Our approach incorporates anatomical info...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - December 12, 2023 Category: Radiology Authors: Jiazhen Zhang Rajesh Venkataraman Lawrence H Staib John A Onofrey Source Type: research