# HALOS: Hallucination-free Organ Segmentation after Organ Resection Surgery

arXiv: https://arxiv.org/abs/2303.07717 · HTML: https://mlsanity.com/paper/2303.07717/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 24 references that are in the map.
- First posted on arXiv 14 Mar 2023.

## Builds on (most cited first, 13 of 13)

- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Domain Adaptation for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2102.09508/) (2021)
- [Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation](https://mlsanity.com/paper/1705.08302/) (2017)
- [Prior-aware Neural Network for Partially-Supervised Multi-Organ Segmentation](https://mlsanity.com/paper/1904.06346/) (2019)
- [JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation](https://mlsanity.com/paper/2004.07054/) (2020)
- [Abdominal multi-organ segmentation with organ-attention networks and statistical fusion](https://mlsanity.com/paper/1804.08414/) (2018)
- [Y-Net: Joint Segmentation and Classification for Diagnosis of Breast Biopsy Images](https://mlsanity.com/paper/1806.01313/) (2018)
- [QuickNAT: A Fully Convolutional Network for Quick and Accurate Segmentation of Neuroanatomy](https://mlsanity.com/paper/1801.04161/) (2018)
- [Deep Learning Based Brain Tumor Segmentation: A Survey](https://mlsanity.com/paper/2007.09479/) (2020)
- [Hierarchical 3D fully convolutional networks for multi-organ segmentation](https://mlsanity.com/paper/1704.06382/) (2017)
- [Deep Learning with Mixed Supervision for Brain Tumor Segmentation](https://mlsanity.com/paper/1812.04571/) (2018)
- [Fully Automated Multi-Organ Segmentation in Abdominal Magnetic Resonance Imaging with Deep Neural Networks](https://mlsanity.com/paper/1912.11000/) (2019)
- [The Dice loss in the context of missing or empty labels: Introducing Phi and ε](https://mlsanity.com/paper/2207.09521/) (2022)

## Built on by (most cited first, 3 of 3)

- [Rethinking Abdominal Organ Segmentation (RAOS) in the clinical scenario: A robustness evaluation benchmark with challenging cases](https://mlsanity.com/paper/2406.13674/) (2024)
- [Multi-head automated segmentation by incorporating detection head into the contextual layer neural network](https://mlsanity.com/paper/2602.02471/) (2026)
- [Bound-Aware Per-Organ Recall Risk Control for Multi-Organ CT Segmentation under Clinical Domain Shift](https://mlsanity.com/paper/2608.18193/) (2026)

## Abstract (arXiv)

The wide range of research in deep learning-based medical image segmentation pushed the boundaries in a multitude of applications. A clinically relevant problem that received less attention is the handling of scans with irregular anatomy, e.g., after organ resection. State-of-the-art segmentation models often lead to organ hallucinations, i.e., false-positive predictions of organs, which cannot be alleviated by oversampling or post-processing. Motivated by the increasing need to develop robust deep learning models, we propose HALOS for abdominal organ segmentation in MR images that handles cases after organ resection surgery. To this end, we combine missing organ classification and multi-organ segmentation tasks into a multi-task model, yielding a classification-assisted segmentation pipeline. The segmentation network learns to incorporate knowledge about organ existence via feature fusion modules. Extensive experiments on a small labeled test set and large-scale UK Biobank data demonstrate the effectiveness of our approach in terms of higher segmentation Dice scores and near-to-zero false positive prediction rate.