Deep Volumetric Universal Lesion Detection using Light-Weight Pseudo 3D Convolution and Surface Point Regression

• URL: http://arxiv.org/abs/2008.13254v1
• Date: Sun, 30 Aug 2020 19:42:06 GMT
• Title: Deep Volumetric Universal Lesion Detection using Light-Weight Pseudo 3D Convolution and Surface Point Regression
• Authors: Jinzheng Cai, Ke Yan, Chi-Tung Cheng, Jing Xiao, Chien-Hung Liao, Le Lu, Adam P. Harrison
• Abstract summary: Computer-aided lesion/significant-findings detection techniques are at the core of medical imaging. We propose a novel deep anchor-free one-stage VULD framework that incorporates (1) P3DC operators to recycle the architectural configurations and pre-trained weights from the off-the-shelf 2D networks. New SPR method to effectively regress the 3D lesion spatial extents by pinpointing their representative key points on lesion surfaces.
• Score: 23.916776570010285
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Identifying, measuring and reporting lesions accurately and comprehensively from patient CT scans are important yet time-consuming procedures for physicians. Computer-aided lesion/significant-findings detection techniques are at the core of medical imaging, which remain very challenging due to the tremendously large variability of lesion appearance, location and size distributions in 3D imaging. In this work, we propose a novel deep anchor-free one-stage VULD framework that incorporates (1) P3DC operators to recycle the architectural configurations and pre-trained weights from the off-the-shelf 2D networks, especially ones with large capacities to cope with data variance, and (2) a new SPR method to effectively regress the 3D lesion spatial extents by pinpointing their representative key points on lesion surfaces. Experimental validations are first conducted on the public large-scale NIH DeepLesion dataset where our proposed method delivers new state-of-the-art quantitative performance. We also test VULD on our in-house dataset for liver tumor detection. VULD generalizes well in both large-scale and small-sized tumor datasets in CT imaging.

Related papers

• 3D-CT-GPT: Generating 3D Radiology Reports through Integration of Large Vision-Language Models [51.855377054763345]
  This paper introduces 3D-CT-GPT, a Visual Question Answering (VQA)-based medical visual language model for generating radiology reports from 3D CT scans. Experiments on both public and private datasets demonstrate that 3D-CT-GPT significantly outperforms existing methods in terms of report accuracy and quality.
  arXiv  Detail & Related papers  (2024-09-28T12:31:07Z)
• Diff3Dformer: Leveraging Slice Sequence Diffusion for Enhanced 3D CT Classification with Transformer Networks [5.806035963947936]
  We propose a Diffusion-based 3D Vision Transformer (Diff3Dformer) to aggregate repetitive information within 3D CT scans. Our method exhibits improved performance on two different scales of small datasets of 3D lung CT scans.
  arXiv  Detail & Related papers  (2024-06-24T23:23:18Z)
• Super-resolution of biomedical volumes with 2D supervision [84.5255884646906]
  Masked slice diffusion for super-resolution exploits the inherent equivalence in the data-generating distribution across all spatial dimensions of biological specimens. We focus on the application of SliceR to stimulated histology (SRH), characterized by its rapid acquisition of high-resolution 2D images but slow and costly optical z-sectioning.
  arXiv  Detail & Related papers  (2024-04-15T02:41:55Z)
• 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)
• View-Disentangled Transformer for Brain Lesion Detection [50.4918615815066]
  We propose a novel view-disentangled transformer to enhance the extraction of MRI features for more accurate tumour detection. First, the proposed transformer harvests long-range correlation among different positions in a 3D brain scan. Second, the transformer models a stack of slice features as multiple 2D views and enhance these features view-by-view. Third, we deploy the proposed transformer module in a transformer backbone, which can effectively detect the 2D regions surrounding brain lesions.
  arXiv  Detail & Related papers  (2022-09-20T11:58:23Z)
• 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)
• DRDrV3: Complete Lesion Detection in Fundus Images Using Mask R-CNN, Transfer Learning, and LSTM [2.9360071145551068]
  We propose a new lesion detection architecture, comprising of two sub-modules, which is an optimal solution to detect and find lesions caused by Diabetic Retinopathy (DR) We also use two popular evaluation criteria to evaluate the outputs of our models, which are intersection over union (IOU) and mean average precision (mAP) We hypothesize that this new solution enables specialists to detect lesions with high confidence and estimate the severity of the damage with high accuracy.
  arXiv  Detail & Related papers  (2021-08-18T11:36:37Z)
• 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.