The avant-garde composition'ORGAN /ASLSP' is being stretched to its limits. Breakthroughs, discoveries, and DIY tips sent six days a week. On September 5, 2001, a concert started inside a medieval church-and it continues to this day. If all goes as planned, the performance won't finish for another 616 years . One may expect a composition like John Cage's to encompass thousands, if not millions of pages of musical notation--but as its name implies, it's the exact opposite.

The world's longest-living vertebrates maintain their vision for centuries. Breakthroughs, discoveries, and DIY tips sent every weekday. The Greenland shark () is well-known for its impressive lifespan. Marine biologists believe the world's longest-living vertebrate often reaches over 400 years old, and possibly lives even longer. But while the shark isn't known for its vision, a lot could be learned from the deep-sea predator's eyes. According to new research recently published in the journal, the Greenland shark retained its visual organs throughout millions of years of evolution for a reason.

Despite the considerable progress in automatic abdominal multi-organ segmentation from CT/MRI scans in recent years, a comprehensive evaluation of the models' capabilities is hampered by the lack of a large-scale benchmark from diverse clinical scenarios. Constraint by the high cost of collecting and labeling 3D medical data, most of the deep learning models to date are driven by datasets with a limited number of organs of interest or samples, which still limits the power of modern deep models and makes it difficult to provide a fully comprehensive and fair estimate of various methods. To mitigate the limitations, we present AMOS, a large-scale, diverse, clinical dataset for abdominal organ segmentation. AMOS provides 500 CT and 100 MRI scans collected from multi-center, multi-vendor, multi-modality, multi-phase, multi-disease patients, each with voxel-level annotations of 15 abdominal organs, providing challenging examples and test-bed for studying robust segmentation algorithms under diverse targets and scenarios. We further benchmark several state-of-the-art medical segmentation models to evaluate the status of the existing methods on this new challenging dataset. We have made our datasets, benchmark servers, and baselines publicly available, and hope to inspire future research. Information can be found at https://amos22.grand-challenge.org.

Science Corporation, founded by former Neuralink president Max Hodak, has unveiled a prototype machine to extend the life of organs for longer periods. Science Corporation, the brain-computer interface startup founded in 2021 by former Neuralink president Max Hodak, is launching a new division of the company with the goal of extending the life of human organs. Alameda, California-based Science is aiming to improve on current perfusion systems that continuously circulate blood through vital organs when they can no longer function on their own. The technology is used to preserve organs for transplant and as a life-support measure for patients when the heart and lungs stop working, but it's clunky and costly. Science wants to make a smaller, more portable system that could provide long-term support.

Foundation models for medical image segmentation struggle under out-of-distribution (OOD) shifts, often producing fragmented false positives on OOD tumors. We introduce R$^{2}$Seg, a training-free framework for robust OOD tumor segmentation that operates via a two-stage Reason-and-Reject process. First, the Reason step employs an LLM-guided anatomical reasoning planner to localize organ anchors and generate multi-scale ROIs. Second, the Reject step applies two-sample statistical testing to candidates generated by a frozen foundation model (BiomedParse) within these ROIs. This statistical rejection filter retains only candidates significantly different from normal tissue, effectively suppressing false positives. Our framework requires no parameter updates, making it compatible with zero-update test-time augmentation and avoiding catastrophic forgetting. On multi-center and multi-modal tumor segmentation benchmarks, R$^{2}$Seg substantially improves Dice, specificity, and sensitivity over strong baselines and the original foundation models. Code are available at https://github.com/Eurekashen/R2Seg.