CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection

• URL: http://arxiv.org/abs/2301.00785v5
• Date: Thu, 17 Aug 2023 15:37:32 GMT
• Title: CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection
• Authors: Jie Liu, Yixiao Zhang, Jie-Neng Chen, Junfei Xiao, Yongyi Lu, Bennett A. Landman, Yixuan Yuan, Alan Yuille, Yucheng Tang, Zongwei Zhou
• Abstract summary: We propose the CLIP-Driven Universal Model, which incorporates text embedding learned from Contrastive Language-Image Pre-training to segmentation models. The proposed model is developed from an assembly of 14 datasets, using a total of 3,410 CT scans for training and then evaluated on 6,162 external CT scans from 3 additional datasets.
• Score: 36.08551407926805
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: An increasing number of public datasets have shown a marked impact on automated organ segmentation and tumor detection. However, due to the small size and partially labeled problem of each dataset, as well as a limited investigation of diverse types of tumors, the resulting models are often limited to segmenting specific organs/tumors and ignore the semantics of anatomical structures, nor can they be extended to novel domains. To address these issues, we propose the CLIP-Driven Universal Model, which incorporates text embedding learned from Contrastive Language-Image Pre-training (CLIP) to segmentation models. This CLIP-based label encoding captures anatomical relationships, enabling the model to learn a structured feature embedding and segment 25 organs and 6 types of tumors. The proposed model is developed from an assembly of 14 datasets, using a total of 3,410 CT scans for training and then evaluated on 6,162 external CT scans from 3 additional datasets. We rank first on the Medical Segmentation Decathlon (MSD) public leaderboard and achieve state-of-the-art results on Beyond The Cranial Vault (BTCV). Additionally, the Universal Model is computationally more efficient (6x faster) compared with dataset-specific models, generalized better to CT scans from varying sites, and shows stronger transfer learning performance on novel tasks.

Related papers

• TotalSegmentator MRI: Sequence-Independent Segmentation of 59 Anatomical Structures in MR images [62.53931644063323]
  In this study we extended the capabilities of TotalSegmentator to MR images. We trained an nnU-Net segmentation algorithm on this dataset and calculated similarity coefficients (Dice) to evaluate the model's performance. The model significantly outperformed two other publicly available segmentation models (Dice score 0.824 versus 0.762; p0.001 and 0.762 versus 0.542; p)
  arXiv  Detail & Related papers  (2024-05-29T20:15:54Z)
• Universal and Extensible Language-Vision Models for Organ Segmentation and Tumor Detection from Abdominal Computed Tomography [50.08496922659307]
  We propose a universal framework enabling a single model, termed Universal Model, to deal with multiple public datasets and adapt to new classes. Firstly, we introduce a novel language-driven parameter generator that leverages language embeddings from large language models. Secondly, the conventional output layers are replaced with lightweight, class-specific heads, allowing Universal Model to simultaneously segment 25 organs and six types of tumors.
  arXiv  Detail & Related papers  (2024-05-28T16:55:15Z)
• RadGenome-Chest CT: A Grounded Vision-Language Dataset for Chest CT Analysis [56.57177181778517]
  RadGenome-Chest CT is a large-scale, region-guided 3D chest CT interpretation dataset based on CT-RATE. We leverage the latest powerful universal segmentation and large language models to extend the original datasets.
  arXiv  Detail & Related papers  (2024-04-25T17:11:37Z)
• One Model to Rule them All: Towards Universal Segmentation for Medical Images with Text Prompts [62.55349777609194]
  We aim to build up a model that can Segment Anything in radiology scans, driven by Text prompts, termed as SAT. We build up the largest and most comprehensive segmentation dataset for training, by collecting over 22K 3D medical image scans. We have trained SAT-Nano (110M parameters) and SAT-Pro (447M parameters) demonstrating comparable performance to 72 specialist nnU-Nets trained on each dataset/subsets.
  arXiv  Detail & Related papers  (2023-12-28T18:16:00Z)
• 3D Medical Image Segmentation based on multi-scale MPU-Net [5.393743755706745]
  This paper proposes a tumor segmentation model MPU-Net for patient volume CT images. It is inspired by Transformer with a global attention mechanism. Compared with the benchmark model U-Net, MPU-Net shows excellent segmentation results.
  arXiv  Detail & Related papers  (2023-07-11T20:46:19Z)
• Continual Segment: Towards a Single, Unified and Accessible Continual Segmentation Model of 143 Whole-body Organs in CT Scans [31.388497540849297]
  We propose a new architectural CSS learning framework to learn a single deep segmentation model for segmenting a total of 143 whole-body organs. We trained and validated on 3D CT scans of 2500+ patients from four datasets, our single network can segment total 143 whole-body organs with very high accuracy.
  arXiv  Detail & Related papers  (2023-02-01T00:49:21Z)
• TotalSegmentator: robust segmentation of 104 anatomical structures in CT images [48.50994220135258]
  We present a deep learning segmentation model for body CT images. The model can segment 104 anatomical structures relevant for use cases such as organ volumetry, disease characterization, and surgical or radiotherapy planning.
  arXiv  Detail & Related papers  (2022-08-11T15:16:40Z)
• A New Probabilistic V-Net Model with Hierarchical Spatial Feature Transform for Efficient Abdominal Multi-Organ Segmentation [15.26560999964979]
  We propose a probabilistic multi-organ segmentation network with hierarchical spatial-wise feature modulation to capture flexible organ semantic variants. The proposed method is trained on the publicly available AbdomenCT-1K dataset and evaluated on two other open datasets.
  arXiv  Detail & Related papers  (2022-08-02T11:51:46Z)
• Generalized Organ Segmentation by Imitating One-shot Reasoning using Anatomical Correlation [55.1248480381153]
  We propose OrganNet which learns a generalized organ concept from a set of annotated organ classes and then transfer this concept to unseen classes. We show that OrganNet can effectively resist the wide variations in organ morphology and produce state-of-the-art results in one-shot segmentation task.
  arXiv  Detail & Related papers  (2021-03-30T13:41:12Z)
• Weakly Supervised 3D Classification of Chest CT using Aggregated Multi-Resolution Deep Segmentation Features [5.938730586521215]
  Weakly supervised disease classification of CT imaging suffers from poor localization owing to case-level annotations. We propose a medical classifier that leverages semantic structural concepts learned via multi-resolution segmentation feature maps.
  arXiv  Detail & Related papers  (2020-10-31T00:16:53Z)

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.