# Accurate Weakly-Supervised Deep Lesion Segmentation using Large-Scale Clinical Annotations: Slice-Propagated 3D Mask Generation from 2D RECIST

arXiv: https://arxiv.org/abs/1807.01172 · HTML: https://mlsanity.com/paper/1807.01172/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2018.

## Built on by (most cited first, 17 of 17)

- [The ULS23 Challenge: a Baseline Model and Benchmark Dataset for 3D Universal Lesion Segmentation in Computed Tomography](https://mlsanity.com/paper/2406.05231/) (2024)
- [Weakly-Supervised Universal Lesion Segmentation with Regional Level Set Loss](https://mlsanity.com/paper/2105.01218/) (2021)
- [Weakly Supervised Few-Shot Segmentation Via Meta-Learning](https://mlsanity.com/paper/2109.01693/) (2021)
- [Ultrasound Nodule Segmentation Using Asymmetric Learning with Simple Clinical Annotation](https://mlsanity.com/paper/2404.14852/) (2024)
- [Lesion Segmentation and RECIST Diameter Prediction via Click-driven Attention and Dual-path Connection](https://mlsanity.com/paper/2105.01828/) (2021)
- [SAM-Driven Weakly Supervised Nodule Segmentation with Uncertainty-Aware Cross Teaching](https://mlsanity.com/paper/2407.13553/) (2024)
- [LNQ 2023 challenge: Benchmark of weakly-supervised techniques for mediastinal lymph node quantification](https://mlsanity.com/paper/2408.10069/) (2024)
- [VoxelEmbed: 3D Instance Segmentation and Tracking with Voxel Embedding based Deep Learning](https://mlsanity.com/paper/2106.11480/) (2021)
- [Accurate and Robust Lesion RECIST Diameter Prediction and Segmentation with Transformers](https://mlsanity.com/paper/2208.13113/) (2022)
- [Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations](https://mlsanity.com/paper/2103.06384/) (2021)
- [Learning Fuzzy Clustering for SPECT/CT Segmentation via Convolutional Neural Networks](https://mlsanity.com/paper/2104.08623/) (2021)
- [Weakly Supervised Volumetric Segmentation via Self-taught Shape Denoising Model](https://mlsanity.com/paper/2104.13082/) (2021)
- [RECIST Weakly Supervised Lesion Segmentation via Label-Space Co-Training](https://mlsanity.com/paper/2303.00205/) (2023)
- [Optimizing Universal Lesion Segmentation: State Space Model-Guided Hierarchical Networks with Feature Importance Adjustment](https://mlsanity.com/paper/2404.17235/) (2024)
- [Weakly Supervised Segmentation of Vertebral Bodies with Iterative Slice-propagation](https://mlsanity.com/paper/2402.08892/) (2024)
- [Adapting Foundation Models for Annotation-Efficient Adnexal Mass Segmentation in Cine Images](https://mlsanity.com/paper/2604.08045/) (2026)
- [A Unified 2D Framework for DeepLesion Detection, Segmentation and Short Report Generation](https://mlsanity.com/paper/2608.02805/) (2026)

## Abstract (arXiv)

Volumetric lesion segmentation from computed tomography (CT) images is a powerful means to precisely assess multiple time-point lesion/tumor changes. However, because manual 3D segmentation is prohibitively time consuming, current practices rely on an imprecise surrogate called response evaluation criteria in solid tumors (RECIST). Despite their coarseness, RECIST markers are commonly found in current hospital picture and archiving systems (PACS), meaning they can provide a potentially powerful, yet extraordinarily challenging, source of weak supervision for full 3D segmentation. Toward this end, we introduce a convolutional neural network (CNN) based weakly supervised slice-propagated segmentation (WSSS) method to 1) generate the initial lesion segmentation on the axial RECIST-slice; 2) learn the data distribution on RECIST-slices; 3) extrapolate to segment the whole lesion slice by slice to finally obtain a volumetric segmentation. To validate the proposed method, we first test its performance on a fully annotated lymph node dataset, where WSSS performs comparably to its fully supervised counterparts. We then test on a comprehensive lesion dataset with 32,735 RECIST marks, where we report a mean Dice score of 92% on RECIST-marked slices and 76% on the entire 3D volumes.