# MOSAIC: A Multi-View 2.5D Organ Slice Selector with Cross-Attentional Reasoning for Anatomically-Aware CT Localization in Medical Organ Segmentation

arXiv: https://arxiv.org/abs/2505.10672 · HTML: https://mlsanity.com/paper/2505.10672/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 51 references that are in the map.
- First posted on arXiv 15 May 2025.

## Builds on (most cited first, 25 of 25)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection](https://mlsanity.com/paper/2301.00785/) (2023)
- [Diffusion Deformable Model for 4D Temporal Medical Image Generation](https://mlsanity.com/paper/2206.13295/) (2022)
- [Bridging 2D and 3D Segmentation Networks for Computation Efficient Volumetric Medical Image Segmentation: An Empirical Study of 2.5D Solutions](https://mlsanity.com/paper/2010.06163/) (2020)
- [Volumetric Attention for 3D Medical Image Segmentation and Detection](https://mlsanity.com/paper/2004.01997/) (2020)
- [Test-Time Adaptation with SaLIP: A Cascade of SAM and CLIP for Zero shot Medical Image Segmentation](https://mlsanity.com/paper/2404.06362/) (2024)
- [Organ at Risk Segmentation for Head and Neck Cancer using Stratified Learning and Neural Architecture Search](https://mlsanity.com/paper/2004.08426/) (2020)
- [SMAFormer: Synergistic Multi-Attention Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2409.00346/) (2024)
- [Automatic Segmentation of Organs-at-Risk from Head-and-Neck CT using Separable Convolutional Neural Network with Hard-Region-Weighted Loss](https://mlsanity.com/paper/2102.01897/) (2021)
- [A Flexible 2.5D Medical Image Segmentation Approach with In-Slice and Cross-Slice Attention](https://mlsanity.com/paper/2405.00130/) (2024)
- [GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2411.13147/) (2024)
- [Fully Automated Multi-Organ Segmentation in Abdominal Magnetic Resonance Imaging with Deep Neural Networks](https://mlsanity.com/paper/1912.11000/) (2019)
- [CAN3D: Fast 3D Medical Image Segmentation via Compact Context Aggregation](https://mlsanity.com/paper/2109.05443/) (2021)
- [A Novel Hybrid Convolutional Neural Network for Accurate Organ Segmentation in 3D Head and Neck CT Images](https://mlsanity.com/paper/2109.12634/) (2021)
- [CSAM: A 2.5D Cross-Slice Attention Module for Anisotropic Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2311.04942/) (2023)
- [Thickened 2D Networks for Efficient 3D Medical Image Segmentation](https://mlsanity.com/paper/1904.01150/) (2019)
- [Tackling the Class Imbalance Problem of Deep Learning Based Head and Neck Organ Segmentation](https://mlsanity.com/paper/2201.01636/) (2022)
- [Improved Abdominal Multi-Organ Segmentation via 3D Boundary-Constrained Deep Neural Networks](https://mlsanity.com/paper/2210.04285/) (2022)
- [Evaluation of Multi-Slice Inputs to Convolutional Neural Networks for Medical Image Segmentation](https://mlsanity.com/paper/1912.09287/) (2019)
- [Towards more precise automatic analysis: a comprehensive survey of deep learning-based multi-organ segmentation](https://mlsanity.com/paper/2303.00232/) (2023)
- [Leveraging multi-view data without annotations for prostate MRI segmentation: A contrastive approach](https://mlsanity.com/paper/2308.06477/) (2023)
- [gcDLSeg: Integrating Graph-cut into Deep Learning for Binary Semantic Segmentation](https://mlsanity.com/paper/2312.04713/) (2023)
- [Enhancing Single-Slice Segmentation with 3D-to-2D Unpaired Scan Distillation](https://mlsanity.com/paper/2406.12254/) (2024)
- [Online learning for X-ray, CT or MRI](https://mlsanity.com/paper/2306.06491/) (2023)
- [Deep Learning-Based Automated Workflow for Accurate Segmentation and Measurement of Abdominal Organs in CT Scans](https://mlsanity.com/paper/2503.10717/) (2025)

## Built on by (most cited first, 1 of 1)

- [Prompt-Guided Patch UNet-VAE with Adversarial Supervision for Adrenal Gland Segmentation in Computed Tomography Medical Images](https://mlsanity.com/paper/2509.03188/) (2025)

## Abstract (arXiv)

Efficient and accurate multi-organ segmentation from abdominal CT volumes is a fundamental challenge in medical image analysis. Existing 3D segmentation approaches are computationally and memory intensive, often processing entire volumes that contain many anatomically irrelevant slices. Meanwhile, 2D methods suffer from class imbalance and lack cross-view contextual awareness. To address these limitations, we propose a novel, anatomically-aware slice selector pipeline that reduces input volume prior to segmentation. Our unified framework introduces a vision-language model (VLM) for cross-view organ presence detection using fused tri-slice (2.5D) representations from axial, sagittal, and coronal planes. Our proposed model acts as an "expert" in anatomical localization, reasoning over multi-view representations to selectively retain slices with high structural relevance. This enables spatially consistent filtering across orientations while preserving contextual cues. More importantly, since standard segmentation metrics such as Dice or IoU fail to measure the spatial precision of such slice selection, we introduce a novel metric, Slice Localization Concordance (SLC), which jointly captures anatomical coverage and spatial alignment with organ-centric reference slices. Unlike segmentation-specific metrics, SLC provides a model-agnostic evaluation of localization fidelity. Our model offers substantial improvement gains against several baselines across all organs, demonstrating both accurate and reliable organ-focused slice filtering. These results show that our method enables efficient and spatially consistent organ filtering, thereby significantly reducing downstream segmentation cost while maintaining high anatomical fidelity.