3DLAND: 3D Lesion Abdominal Anomaly Localization Dataset
Mehran Advand, Zahra Dehghanian, Navid Faraji, Reza Barati, Seyed Amir Ahmad Safavi-Naini, Hamid R. Rabiee
TL;DR
3DLAND is a comprehensive 3D abdominal lesion dataset with over 6,000 CT volumes and 20,000 annotations, designed to improve AI models for medical imaging and clinical diagnosis.
Contribution
The paper introduces 3DLAND, a large-scale, multi-organ 3D lesion dataset with a novel pipeline for annotation, enabling robust evaluation and development of organ-aware segmentation models.
Findings
Surface dice scores exceed 0.75 for annotations
Dataset covers diverse lesion types and demographics
Establishes a new benchmark for 3D abdominal segmentation
Abstract
Existing medical imaging datasets for abdominal CT often lack three-dimensional annotations, multi-organ coverage, or precise lesion-to-organ associations, hindering robust representation learning and clinical applications. To address this gap, we introduce 3DLAND, a large-scale benchmark dataset comprising over 6,000 contrast-enhanced CT volumes with over 20,000 high-fidelity 3D lesion annotations linked to seven abdominal organs: liver, kidneys, pancreas, spleen, stomach, and gallbladder. Our streamlined three-phase pipeline integrates automated spatial reasoning, prompt-optimized 2D segmentation, and memory-guided 3D propagation, validated by expert radiologists with surface dice scores exceeding 0.75. By providing diverse lesion types and patient demographics, 3DLAND enables scalable evaluation of anomaly detection, localization, and cross-organ transfer learning for medical AI. Our…
Peer Reviews
Decision·ICLR 2026 Conference Desk Rejected Submission
- Originality and Pipeline Design: The end-to-end pipeline is a major strength. The combination of automated spatial reasoning for organ assignment (Phase I) with carefully optimized prompt-based segmentation (Phase II) and advanced 3D propagation (Phase III) represents a significant methodological innovation. - Rigor and Scale: The empirical validation is exceptional. The paper demonstrates high performance (e.g., Dice ~0.81 in 2D, ~0.70 in 3D) on a very large and diverse dataset (20,000+
- Generalization to External Data: The pipeline is developed and validated primarily on the DeepLesion dataset. Testing its performance on external cohorts from different institutions or with different CT scanning protocols would strengthen the claim of robustness and generalizability. - Granularity of Lesion Characterization: While the dataset covers various lesion types (cysts, tumors), it currently lacks finer-grained annotations (e.g., benign vs. malignant classification, specific pa
1. This work proposes the first large-scale, organ-aware 3D lesion dataset (3DLAND) linking over 20,000 lesions to seven abdominal organs, filling the gap left by datasets like DeepLesion and ULS23 and enabling clinically interpretable cross-organ benchmarking. 2. This work introduces a three-phase automated pipeline combining spatial reasoning, optimized 2D prompts, and memory-guided 3D propagation, offering an efficient and accurate method to transform 2D lesion boxes into expert-level 3D mas
1. Lack of external validation across imaging domains. All data originate from DeepLesion, limiting generalizability across scanners and contrast phases. No cross-dataset tests (e.g., AMOS22, AbdomenCT-1K) are provided to verify whether fixed IoU and distance thresholds remain stable under varied acquisition conditions. 2. Lack of methodological novelty beyond existing SAM frameworks. The pipeline mainly combines MONAI, MedSAM1, and MedSAM2 without substantive algorithmic innovation. Prior work
The paper provides a large dataset of 6000 3D volumes with lesion and organ segmentations. This has the potential to yield a reliable baseline the 3D medical image computing domain can optimize their automated methods on, as currently one is required to train multiple methods on multiple segmentation benchmarks, with different methods often having different patient splits or using noisy dataset [1]. Hence I find the concept of the dataset very interesting. [1]: Isensee, Fabian, et al. "nnu-ne
My main concern of this paper are the methods used for automated segmentation generation and the final segmentation mask quality, as previous datasets like AbdomenAtlas who also followed a sem-supervised segmentation procedure had substantial quality issues: ### Organ segmentation While I am not familiar with the MONAI framework used for organ segmentation, the recent nnU-Net Revisited [1] and Touchstone Benchmark [2] both showed that methods trained in MONAI were less powerful than those usin
1. Multi-organ, lesion-level 3D annotation is a genuine unmet need. Existing datasets (e.g., LiTS, KiTS) are organ-specific; DeepLesion lacks 3D masks. 3DLAND’s attempt to bridge this gap is highly relevant. 2. The integration of prompt-based segmentation (MedSAM) with organ-aware spatial reasoning offers a pragmatic approach to semi-automated annotation, potentially reducing expert burden. 3. The paper reports Dice, Surface Dice, and ablation studies on prompt design and assignment thresholds,
1. Several cited works appear non-existent or AI-generated, including but not limited to: - Ke Yan, Xiaohuan Wang, Mahmood Bagheri, Le Lu, and Ronald M. Summers. LesaNet: Robust lesion attribute segmentation in CT scans. In Medical Image Computing and Computer Assisted Intervention (MICCAI), pp. 622–630. Springer, 2019. - Xiyue Zhao, Fangyu Tang, Xin Wang, Yu Song, Wenjia Zhang, and Jian Xiao. Abdomenatlas-8k: A hierarchical 3d abdominal multi-organ segmentation benchmark, 2023a. - Zhi Zhao, Ziy
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Taxonomy
TopicsCOVID-19 diagnosis using AI · Advanced Neural Network Applications · AI in cancer detection