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arXiv Computer Vision

Region-Weighted Losses and Model Fusion for Cross-Modal PET Attenuation Correction

The paper presents a method for the BIC-MAC challenge, aiming to generate pseudo‑CT images from NAC‑PET, DIXON MRI, and a topogram, and to evaluate both the pseudo‑CT and the resulting attenuation‑corrected PET. The authors improve upon a 3D U‑Net baseline by focusing on loss design: they compute an L1 error in the Carney attenuation‑coefficient space, weighted by anatomical region, and incorporate DIXON MRI as additional input only after this loss was applied. Finally, they fuse two independently trained models via a fixed convex combination, achieving better performance than either model alone and topping the public validation leaderboard.

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arXiv Computer Vision
Aug 25

Multimodal pseudo-CT synthesis for PET attenuation correction using separate modality encoding and topogram conditioning

The paper presents a multimodal 3D patch-based U‑Net for generating pseudo‑CT images from non‑attenuation‑corrected PET (NAC‑PET), MRI, and 2D topograms. It employs separate PET and MR encoders, multi‑scale feature fusion, and FiLM‑based topogram conditioning at the bottleneck to integrate complementary cross‑modal information while reducing dependence on precise voxel‑wise correspondence. The authors participated in the BIC‑MAC Challenge and released their final model on GitHub.

By Rory Bell, Artemis Bouzaki, Jiaming Cao, Jasmine Morrison, Chelsea Sargeant
multimodal
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arXiv Computer Vision
Sep 24

Geometry-anchored PET-aware multimodal pseudo-CT synthesis for whole-body attenuation correction: the BIC-MAC Challenge

arXiv:2609.27848v1 Announce Type: new Abstract: The BIC-MAC challenge targets whole-body pseudo-CT synthesis from NAC-PET, Dixon MRI, and a 2D topogram for CT-less PET attenuation correction. We prop...

By Xuan Loc Nguyen, Hoang-Loc Cao, Truong Thanh Hung Nguyen, Phuc Ho, Phuc Truong Loc Nguyen, Nguyen Truong Toan To, Hung Cao
multimodal
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arXiv AI
Sep 18

Bridging Modalities on the Cortex: Surface-based MRI to PET Translation with a Diffusion Bridge

The paper introduces DB‑SUiT, a surface‑based diffusion bridge that translates cortical MRI to PET images directly on the cortical manifold. It employs a conditional spherical U‑shaped vision Transformer to capture multi‑scale surface features and long‑range dependencies while incorporating demographic and subcortical information. Evaluations on two dementia datasets show that the synthesized PET surfaces outperform MRI and PET volumes in automated classification and achieve high diagnostic accuracy in a blinded reader study.

By Yitong Li, Alexandra Samoylova, Fabian Bongratz, Timo Grimmer, Dennis M. Hedderich, Igor Yakushev, Christian Wachinger
llmsdiffusioncomputer-visionmultimodal
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arXiv Computer Vision
Sep 11

Confidence-Calibrating Regularization for Robust Brain MRI Segmentation Under Domain Shift

The paper introduces CalSAM, a lightweight adaptation framework that fine‑tunes only the mask decoder of the Segment Anything Model (SAM) while keeping its encoders frozen. CalSAM employs a Feature Fisher Information Penalty (FIP) to reduce encoder sensitivity to domain shift and a Confidence Misalignment Penalty (CMP) to curb overconfident voxel‑wise errors. Experiments on cross‑center, scanner‑shift, and motion‑corrupted brain MRI datasets show significant gains in Dice similarity coefficient, Hausdorff distance, and expected calibration error, with only a modest training‑time overhead.

By Behraj Khan, Tahir Qasim Syed, Syed Ahmad Chan Bukhari
computer-vision
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arXiv AI
Aug 13

A Neighborhood Attention Transformer Network for Enhanced 3D Segmentation of the Left Anterior Descending Artery

arXiv:2608. 12274v1 Announce Type: cross Abstract: Background: Accurate segmentation of the Left Anterior Descending (LAD) artery in 3D free-breathing, non-contrast CT is critical for cardiac dose sparing in thoracic radiotherapy.

By Rafi Ibn Sultan, Chengyin Li, Yiannos Demetriou, Ahmed I. Ghanem, Joshua P. Kim, Justine Cunningham, Hassan Bagher-Ebadian, Dongxiao Zhu, Kundan S. Thind
llmscomputer-visionfine-tuning
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Hugging Face Trending Papers
Aug 6

ALTER: Modeling Longitudinal Changes via Regional Differencing for 3D CT Report Generation

Computed tomography (CT) is widely used for clinical diagnosis and longitudinal follow-up, yet automatically generating accurate and complete radiology reports from three-dimensional (3D) CT remains challenging. Existing methods improve fine-grained correspondence between images and text by modeling anatomical regions, but remain centered on the current examination.

benchmarks
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arXiv AI
Sep 1

Extending TotalSegmentator: Predicting Patient and Acquisition Characteristics from CT and MR Images

arXiv:2608.29348v1 Announce Type: new Abstract: Background: Patient details and acquisition metadata are important for clinical decisions, image quality control, and automated research pipelines, but...

By Jakob Wasserthal, Joshy Cyriac, Michael Bach, Kimia Mozahheb Yousefi, Minh-Son To, M\'at\'e Sik, C\'edric H\'emon, Thomas Weikert, Martin Segeroth
computer-vision
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