# YOLO-PVC: 2D-to-3D Consolidation of Slice-wise Detections for Volumetric Liver Tumor Localization in MRI

arXiv: https://arxiv.org/abs/2608.04642 · HTML: https://mlsanity.com/paper/2608.04642/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 24 references that are in the map.
- First posted on arXiv 5 Aug 2026.

## Builds on (most cited first, 10 of 10)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Object as Query: Lifting any 2D Object Detector to 3D Detection](https://mlsanity.com/paper/2301.02364/) (2023)
- [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)
- [Weighted boxes fusion: Ensembling boxes from different object detection models](https://mlsanity.com/paper/1910.13302/) (2019)
- [ConvNet-Based Localization of Anatomical Structures in 3D Medical Images](https://mlsanity.com/paper/1704.05629/) (2017)
- [A Flexible 2.5D Medical Image Segmentation Approach with In-Slice and Cross-Slice Attention](https://mlsanity.com/paper/2405.00130/) (2024)
- [Focused Decoding Enables 3D Anatomical Detection by Transformers](https://mlsanity.com/paper/2207.10774/) (2022)
- [3D Bounding Box Detection in Volumetric Medical Image Data: A Systematic Literature Review](https://mlsanity.com/paper/2012.05745/) (2020)

## Abstract (arXiv)

Slice-wise 2D object detectors are increasingly applied to volumetric data due to their computational efficiency and scalability, yet they often yield fragmented and unstable predictions along the depth axis. We propose YOLO-PVC, a lightweight and model-agnostic framework for 2D-to-3D consolidation of slice-wise detections. The method enforces depth continuity, aggregates bounding box coordinates using robust percentile statistics, and further refines axial extent through a lightweight MLP-based calibration module. Unlike na"ive stacking or averaging strategies, YOLO-PVC explicitly addresses missing detections and outlier slices along the depth dimension. Experiments on 3D liver MRI volumes across three tumor categories demonstrate consistent improvements over multiple aggregation baselines. The heuristic PVC achieves an overall IoU3D of 0.665, while the calibrated variant further improves performance to 0.710, with high planar overlap (BEV IoU ≈ 0.78). These results demonstrate that structured geometric consolidation provides an effective and practical solution for volumetric liver tumor localization in clinical MRI.