3DLAND: 3D Lesion Abdominal Anomaly Localization Dataset

• URL: http://arxiv.org/abs/2602.12820v1
• Date: Fri, 13 Feb 2026 11:08:15 GMT
• Title: 3DLAND: 3D Lesion Abdominal Anomaly Localization Dataset
• Authors: Mehran Advand, Zahra Dehghanian, Navid Faraji, Reza Barati, Seyed Amir Ahmad Safavi-Naini, Hamid R. Rabiee,
• Abstract summary: 3DLAND is 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, pancreas, kidneys, spleen, stomach, and gallbladder.<n>Our 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.<n>Our dataset establishes a new benchmark for evaluating organ-aware 3D segmentation models, paving the way for advancements in healthcare-oriented AI.
• Score: 8.974431489804337
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 dataset establishes a new benchmark for evaluating organ-aware 3D segmentation models, paving the way for advancements in healthcare-oriented AI. To facilitate reproducibility and further research, the 3DLAND dataset and implementation code are publicly available at https://mehrn79.github.io/3DLAND.

Related papers

• Tracing 3D Anatomy in 2D Strokes: A Multi-Stage Projection Driven Approach to Cervical Spine Fracture Identification [6.060672815761177]
  This study explores the viability of 2D projection-based vertebra segmentation for vertebra-level fracture detection in 3D CT volumes.<n>Regions of interest are identified using the YOLOv8 model from all views and combined to approximate the 3D cervical spine area.<n>A DenseNet121-Unet-based multi-label segmentation leveraging variance- and energy-based projections achieves a Dice score of 87.86 percent.
  arXiv  Detail & Related papers  (2026-01-21T18:15:47Z)
• Improving 3D Medical Image Segmentation at Boundary Regions using Local Self-attention and Global Volume Mixing [14.0825980706386]
  Volumetric medical image segmentation is a fundamental problem in medical image analysis where the objective is to accurately classify a given 3D volumetric medical image with voxel-level precision. In this work, we propose a novel hierarchical encoder-decoder-based framework that strives to explicitly capture the local and global dependencies for 3D medical image segmentation. The proposed framework exploits local volume-based self-attention to encode the local dependencies at high resolution and introduces a novel volumetric-mixer to capture the global dependencies at low-resolution feature representations.
  arXiv  Detail & Related papers  (2024-10-20T11:08:38Z)
• Devil is in Details: Locality-Aware 3D Abdominal CT Volume Generation for Self-Supervised Organ Segmentation [5.395145012900144]
  Locality-Aware Diffusion (Lad) is a novel method tailored for exquisite 3D abdominal CT volume generation.<n>We demonstrate remarkable fidelity in reproducing abdominal structures, achieving a decrease in FID score from 0.0034 to 0.0002 on AbdomenCT-1K dataset.<n>These results underscore the potential of synthetic data to advance self-supervised learning in medical image analysis.
  arXiv  Detail & Related papers  (2024-09-30T14:35:53Z)
• Generative Enhancement for 3D Medical Images [74.17066529847546]
  We propose GEM-3D, a novel generative approach to the synthesis of 3D medical images. Our method begins with a 2D slice, noted as the informed slice to serve the patient prior, and propagates the generation process using a 3D segmentation mask. By decomposing the 3D medical images into masks and patient prior information, GEM-3D offers a flexible yet effective solution for generating versatile 3D images.
  arXiv  Detail & Related papers  (2024-03-19T15:57:04Z)
• Multi-dimension unified Swin Transformer for 3D Lesion Segmentation in Multiple Anatomical Locations [1.7413461132662074]
  We propose a novel model, denoted a multi-dimension unified Swin transformer (MDU-ST) for 3D lesion segmentation. The network's performance is evaluated by the Dice similarity coefficient (DSC) and Hausdorff distance (HD) using an internal 3D lesion dataset. The proposed method can be used to conduct automated 3D lesion segmentation to assist radiomics and tumor growth modeling studies.
  arXiv  Detail & Related papers  (2023-09-04T21:24:00Z)
• On the Localization of Ultrasound Image Slices within Point Distribution Models [84.27083443424408]
  Thyroid disorders are most commonly diagnosed using high-resolution Ultrasound (US) Longitudinal tracking is a pivotal diagnostic protocol for monitoring changes in pathological thyroid morphology. We present a framework for automated US image slice localization within a 3D shape representation.
  arXiv  Detail & Related papers  (2023-09-01T10:10:46Z)
• 3D unsupervised anomaly detection and localization through virtual multi-view projection and reconstruction: Clinical validation on low-dose chest computed tomography [2.2302915692528367]
  We propose a method based on a deep neural network for computer-aided diagnosis called virtual multi-view projection and reconstruction. The proposed method improves the patient-level anomaly detection by 10% compared with a gold standard based on supervised learning. It localizes the anomaly region with 93% accuracy, demonstrating its high performance.
  arXiv  Detail & Related papers  (2022-06-18T13:22:00Z)
• A unified 3D framework for Organs at Risk Localization and Segmentation for Radiation Therapy Planning [56.52933974838905]
  Current medical workflow requires manual delineation of organs-at-risk (OAR) In this work, we aim to introduce a unified 3D pipeline for OAR localization-segmentation. Our proposed framework fully enables the exploitation of 3D context information inherent in medical imaging.
  arXiv  Detail & Related papers  (2022-03-01T17:08:41Z)
• Automated Model Design and Benchmarking of 3D Deep Learning Models for COVID-19 Detection with Chest CT Scans [72.04652116817238]
  We propose a differentiable neural architecture search (DNAS) framework to automatically search for the 3D DL models for 3D chest CT scans classification. We also exploit the Class Activation Mapping (CAM) technique on our models to provide the interpretability of the results.
  arXiv  Detail & Related papers  (2021-01-14T03:45:01Z)
• Revisiting 3D Context Modeling with Supervised Pre-training for Universal Lesion Detection in CT Slices [48.85784310158493]
  We propose a Modified Pseudo-3D Feature Pyramid Network (MP3D FPN) to efficiently extract 3D context enhanced 2D features for universal lesion detection in CT slices. With the novel pre-training method, the proposed MP3D FPN achieves state-of-the-art detection performance on the DeepLesion dataset. The proposed 3D pre-trained weights can potentially be used to boost the performance of other 3D medical image analysis tasks.
  arXiv  Detail & Related papers  (2020-12-16T07:11:16Z)
• Volumetric Medical Image Segmentation: A 3D Deep Coarse-to-fine Framework and Its Adversarial Examples [74.92488215859991]
  We propose a novel 3D-based coarse-to-fine framework to efficiently tackle these challenges. The proposed 3D-based framework outperforms their 2D counterparts by a large margin since it can leverage the rich spatial information along all three axes. We conduct experiments on three datasets, the NIH pancreas dataset, the JHMI pancreas dataset and the JHMI pathological cyst dataset.
  arXiv  Detail & Related papers  (2020-10-29T15:39:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.