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.

Mesh2SSM: From Surface Meshes to Statistical Shape Models of Anatomy
Med Image Comput Comput Assist Interv. 2023 Oct;14220:615-625. doi: 10.1007/978-3-031-43907-0_59. Epub 2023 Oct 1.ABSTRACTStatistical shape modeling is the computational process of discovering significant shape parameters from segmented anatomies captured by medical images (such as MRI and CT scans), which can fully describe subject-specific anatomy in the context of a population. The presence of substantial non-linear variability in human anatomy often makes the traditional shape modeling process challenging. Deep learning techniques can learn complex non-linear representations of shapes and generate statistical shape mod...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - April 25, 2024 Category: Radiology Authors: Krithika Iyer Shireen Elhabian Source Type: research

Unified Brain MR-Ultrasound Synthesis using Multi-Modal Hierarchical Representations
Med Image Comput Comput Assist Interv. 2023 Oct 13;2023:448-458. doi: 10.1007/978-3-031-43999-5_43.ABSTRACTWe introduce MHVAE, a deep hierarchical variational autoencoder (VAE) that synthesizes missing images from various modalities. Extending multi-modal VAEs with a hierarchical latent structure, we introduce a probabilistic formulation for fusing multi-modal images in a common latent representation while having the flexibility to handle incomplete image sets as input. Moreover, adversarial learning is employed to generate sharper images. Extensive experiments are performed on the challenging problem of joint intra-operat...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - April 24, 2024 Category: Radiology Authors: Reuben Dorent Nazim Haouchine Fryderyk Kogl Samuel Joutard Parikshit Juvekar Erickson Torio Alexandra Golby Sebastien Ourselin Sarah Frisken Tom Vercauteren Tina Kapur William M Wells Source Type: research

Speech Audio Synthesis from Tagged MRI and Non-Negative Matrix Factorization via Plastic Transformer
Med Image Comput Comput Assist Interv. 2023 Oct;14226:435-445. doi: 10.1007/978-3-031-43990-2_41. Epub 2023 Oct 1.ABSTRACTThe tongue's intricate 3D structure, comprising localized functional units, plays a crucial role in the production of speech. When measured using tagged MRI, these functional units exhibit cohesive displacements and derived quantities that facilitate the complex process of speech production. Non-negative matrix factorization-based approaches have been shown to estimate the functional units through motion features, yielding a set of building blocks and a corresponding weighting map. Investigating the lin...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - April 23, 2024 Category: Radiology Authors: Xiaofeng Liu Fangxu Xing Maureen Stone Jiachen Zhuo Sidney Fels Jerry L Prince Georges El Fakhri Jonghye Woo Source Type: research

Pelphix: Surgical Phase Recognition from X-ray Images in Percutaneous Pelvic Fixation
Med Image Comput Comput Assist Interv. 2023 Oct;14228:133-143. doi: 10.1007/978-3-031-43996-4_13. Epub 2023 Oct 1.ABSTRACTSurgical phase recognition (SPR) is a crucial element in the digital transformation of the modern operating theater. While SPR based on video sources is well-established, incorporation of interventional X-ray sequences has not yet been explored. This paper presents Pelphix, a first approach to SPR for X-ray-guided percutaneous pelvic fracture fixation, which models the procedure at four levels of granularity - corridor, activity, view, and frame value - simulating the pelvic fracture fixation workflow a...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - April 15, 2024 Category: Radiology Authors: Benjamin D Killeen Han Zhang Jan Mangulabnan Mehran Armand Russell H Taylor Greg Osgood Mathias Unberath Source Type: research

Breast Ultrasound Tumor Classification Using a Hybrid Multitask CNN-Transformer Network
In this study, we proposed a hybrid multitask deep neural network called Hybrid-MT-ESTAN, designed to perform BUS tumor classification and segmentation using a hybrid architecture composed of CNNs and Swin Transformer components. The proposed approach was compared to nine BUS classification methods and evaluated using seven quantitative metrics on a dataset of 3,320 BUS images. The results indicate that Hybrid-MT-ESTAN achieved the highest accuracy, sensitivity, and F1 score of 82.7%, 86.4%, and 86.0%, respectively.PMID:38601088 | PMC:PMC11006090 | DOI:10.1007/978-3-031-43901-8_33 (Source: MICCAI International Conference o...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - April 11, 2024 Category: Radiology Authors: Bryar Shareef Min Xian Aleksandar Vakanski Haotian Wang Source Type: research

A Unified Deep-Learning-Based Framework for Cochlear Implant Electrode Array Localization
Med Image Comput Comput Assist Interv. 2023 Oct;14228:376-385. doi: 10.1007/978-3-031-43996-4_36. Epub 2023 Oct 1.ABSTRACTCochlear implants (CIs) are neuroprosthetics that can provide a sense of sound to people with severe-to-profound hearing loss. A CI contains an electrode array (EA) that is threaded into the cochlea during surgery. Recent studies have shown that hearing outcomes are correlated with EA placement. An image-guided cochlear implant programming technique is based on this correlation and utilizes the EA location with respect to the intracochlear anatomy to help audiologists adjust the CI settings to improve h...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - April 1, 2024 Category: Radiology Authors: Yubo Fan Jianing Wang Yiyuan Zhao Rui Li Han Liu Robert F Labadie Jack H Noble Benoit M Dawant Source Type: research