Towards a Comprehensive, Efficient and Promptable Anatomic Structure Segmentation Model using 3D Whole-body CT Scans

• URL: http://arxiv.org/abs/2403.15063v2
• Date: Wed, 18 Dec 2024 02:51:53 GMT
• Title: Towards a Comprehensive, Efficient and Promptable Anatomic Structure Segmentation Model using 3D Whole-body CT Scans
• Authors: Heng Guo, Jianfeng Zhang, Jiaxing Huang, Tony C. W. Mok, Dazhou Guo, Ke Yan, Le Lu, Dakai Jin, Minfeng Xu,
• Abstract summary: Segment anything model (SAM) demonstrates strong generalization ability on natural image segmentation. We propose a comprehensive and scalable 3D SAM model for whole-body CT segmentation, named CT-SAM3D. CT-SAM3D is trained using a curated dataset of 1204 CT scans containing 107 whole-body anatomies.
• Score: 23.573958232965104
• License:
• Abstract: Segment anything model (SAM) demonstrates strong generalization ability on natural image segmentation. However, its direct adaptation in medical image segmentation tasks shows significant performance drops. It also requires an excessive number of prompt points to obtain a reasonable accuracy. Although quite a few studies explore adapting SAM into medical image volumes, the efficiency of 2D adaptation methods is unsatisfactory and 3D adaptation methods are only capable of segmenting specific organs/tumors. In this work, we propose a comprehensive and scalable 3D SAM model for whole-body CT segmentation, named CT-SAM3D. Instead of adapting SAM, we propose a 3D promptable segmentation model using a (nearly) fully labeled CT dataset. To train CT-SAM3D effectively, ensuring the model's accurate responses to higher-dimensional spatial prompts is crucial, and 3D patch-wise training is required due to GPU memory constraints. Therefore, we propose two key technical developments: 1) a progressively and spatially aligned prompt encoding method to effectively encode click prompts in local 3D space; and 2) a cross-patch prompt scheme to capture more 3D spatial context, which is beneficial for reducing the editing workloads when interactively prompting on large organs. CT-SAM3D is trained using a curated dataset of 1204 CT scans containing 107 whole-body anatomies and extensively validated using five datasets, achieving significantly better results against all previous SAM-derived models. Code, data, and our 3D interactive segmentation tool with quasi-real-time responses are available at https://github.com/alibaba-damo-academy/ct-sam3d.

Related papers

• RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation [17.69664156349825]
  The Segment Anything Model (SAM) excels at capturing global patterns in 2D natural images but struggles with 3D medical imaging modalities like CT and MRI. We introduce RefSAM3D, which adapts SAM for 3D medical imaging by incorporating a 3D image adapter and cross-modal reference prompt generation. Our contributions advance the application of SAM in accurately segmenting complex anatomical structures in medical imaging.
  arXiv  Detail & Related papers  (2024-12-07T10:22:46Z)
• SAM3D: Zero-Shot Semi-Automatic Segmentation in 3D Medical Images with the Segment Anything Model [3.2554912675000818]
  We introduce SAM3D, a new approach to semi-automatic zero-shot segmentation of 3D images building on the existing Segment Anything Model. We achieve fast and accurate segmentations in 3D images with a four-step strategy involving: user prompting with 3D polylines, volume slicing along multiple axes, slice-wide inference with a pretrained model, and recomposition and refinement in 3D.
  arXiv  Detail & Related papers  (2024-05-10T19:26:17Z)
• 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)
• MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentation [58.53672866662472]
  We introduce a modality-agnostic SAM adaptation framework, named as MA-SAM. Our method roots in the parameter-efficient fine-tuning strategy to update only a small portion of weight increments. By injecting a series of 3D adapters into the transformer blocks of the image encoder, our method enables the pre-trained 2D backbone to extract third-dimensional information from input data.
  arXiv  Detail & Related papers  (2023-09-16T02:41:53Z)
• SAM3D: Segment Anything Model in Volumetric Medical Images [11.764867415789901]
  We introduce SAM3D, an innovative adaptation tailored for 3D volumetric medical image analysis. Unlike current SAM-based methods that segment volumetric data by converting the volume into separate 2D slices for individual analysis, our SAM3D model processes the entire 3D volume image in a unified approach.
  arXiv  Detail & Related papers  (2023-09-07T06:05:28Z)
• 3DSAM-adapter: Holistic adaptation of SAM from 2D to 3D for promptable tumor segmentation [52.699139151447945]
  We propose a novel adaptation method for transferring the segment anything model (SAM) from 2D to 3D for promptable medical image segmentation. Our model can outperform domain state-of-the-art medical image segmentation models on 3 out of 4 tasks, specifically by 8.25%, 29.87%, and 10.11% for kidney tumor, pancreas tumor, colon cancer segmentation, and achieve similar performance for liver tumor segmentation.
  arXiv  Detail & Related papers  (2023-06-23T12:09:52Z)
• TomoSAM: a 3D Slicer extension using SAM for tomography segmentation [62.997667081978825]
  TomoSAM has been developed to integrate the cutting-edge Segment Anything Model (SAM) into 3D Slicer. SAM is a promptable deep learning model that is able to identify objects and create image masks in a zero-shot manner. The synergy between these tools aids in the segmentation of complex 3D datasets from tomography or other imaging techniques.
  arXiv  Detail & Related papers  (2023-06-14T16:13:27Z)
• 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)
• Spatial Context-Aware Self-Attention Model For Multi-Organ Segmentation [18.76436457395804]
  Multi-organ segmentation is one of most successful applications of deep learning in medical image analysis. Deep convolutional neural nets (CNNs) have shown great promise in achieving clinically applicable image segmentation performance on CT or MRI images. We propose a new framework for combining 3D and 2D models, in which the segmentation is realized through high-resolution 2D convolutions.
  arXiv  Detail & Related papers  (2020-12-16T21:39:53Z)
• 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)
• Bidirectional RNN-based Few Shot Learning for 3D Medical Image Segmentation [11.873435088539459]
  We propose a 3D few shot segmentation framework for accurate organ segmentation using limited training samples of the target organ annotation. A U-Net like network is designed to predict segmentation by learning the relationship between 2D slices of support data and a query image. We evaluate our proposed model using three 3D CT datasets with annotations of different organs.
  arXiv  Detail & Related papers  (2020-11-19T01:44:55Z)

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.