A cascaded deep network for automated tumor detection and segmentation in clinical PET imaging of diffuse large B-cell lymphoma

• URL: http://arxiv.org/abs/2403.07092v1
• Date: Mon, 11 Mar 2024 18:36:55 GMT
• Title: A cascaded deep network for automated tumor detection and segmentation in clinical PET imaging of diffuse large B-cell lymphoma
• Authors: Shadab Ahamed, Natalia Dubljevic, Ingrid Bloise, Claire Gowdy, Patrick Martineau, Don Wilson, Carlos F. Uribe, Arman Rahmim, and Fereshteh Yousefirizi
• Abstract summary: We develop and validate a fast and efficient three-step cascaded deep learning model for automated detection and segmentation of DLBCL tumors from PET images. Our model is more effective than a single end-to-end network for segmentation of tumors in whole-body PET images.
• Score: 0.41579653852022364
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate detection and segmentation of diffuse large B-cell lymphoma (DLBCL) from PET images has important implications for estimation of total metabolic tumor volume, radiomics analysis, surgical intervention and radiotherapy. Manual segmentation of tumors in whole-body PET images is time-consuming, labor-intensive and operator-dependent. In this work, we develop and validate a fast and efficient three-step cascaded deep learning model for automated detection and segmentation of DLBCL tumors from PET images. As compared to a single end-to-end network for segmentation of tumors in whole-body PET images, our three-step model is more effective (improves 3D Dice score from 58.9% to 78.1%) since each of its specialized modules, namely the slice classifier, the tumor detector and the tumor segmentor, can be trained independently to a high degree of skill to carry out a specific task, rather than a single network with suboptimal performance on overall segmentation.

Related papers

• Towards Generalizable Tumor Synthesis [48.45704270448412]
  Tumor synthesis enables the creation of artificial tumors in medical images, facilitating the training of AI models for tumor detection and segmentation. This paper made a progressive stride toward generalizable tumor synthesis by leveraging a critical observation. We have ascertained that generative AI models, e.g., Diffusion Models, can create realistic tumors generalized to a range of organs even when trained on a limited number of tumor examples from only one organ.
  arXiv  Detail & Related papers  (2024-02-29T18:57:39Z)
• Contrastive Diffusion Model with Auxiliary Guidance for Coarse-to-Fine PET Reconstruction [62.29541106695824]
  This paper presents a coarse-to-fine PET reconstruction framework that consists of a coarse prediction module (CPM) and an iterative refinement module (IRM) By delegating most of the computational overhead to the CPM, the overall sampling speed of our method can be significantly improved. Two additional strategies, i.e., an auxiliary guidance strategy and a contrastive diffusion strategy, are proposed and integrated into the reconstruction process.
  arXiv  Detail & Related papers  (2023-08-20T04:10:36Z)
• Breast Ultrasound Tumor Classification Using a Hybrid Multitask CNN-Transformer Network [63.845552349914186]
  Capturing global contextual information plays a critical role in breast ultrasound (BUS) image classification. Vision Transformers have an improved capability of capturing global contextual information but may distort the local image patterns due to the tokenization operations. In this study, we proposed a hybrid multitask deep neural network called Hybrid-MT-ESTAN, designed to perform BUS tumor classification and segmentation.
  arXiv  Detail & Related papers  (2023-08-04T01:19:32Z)
• 3DSAM-adapter: Holistic Adaptation of SAM from 2D to 3D for Promptable Medical Image Segmentation [56.50064853710202]
  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)
• Whole-body tumor segmentation of 18F -FDG PET/CT using a cascaded and ensembled convolutional neural networks [2.735686397209314]
  The goal of this study was to report the performance of a deep neural network designed to automatically segment regions suspected of cancer in whole-body 18F-FDG PET/CT images. A cascaded approach was developed where a stacked ensemble of 3D UNET CNN processed the PET/CT images at a fixed 6mm resolution.
  arXiv  Detail & Related papers  (2022-10-14T19:25:56Z)
• EMT-NET: Efficient multitask network for computer-aided diagnosis of breast cancer [58.720142291102135]
  We propose an efficient and light-weighted learning architecture to classify and segment breast tumors simultaneously. We incorporate a segmentation task into a tumor classification network, which makes the backbone network learn representations focused on tumor regions. The accuracy, sensitivity, and specificity of tumor classification is 88.6%, 94.1%, and 85.3%, respectively.
  arXiv  Detail & Related papers  (2022-01-13T05:24:40Z)
• Spatio-Temporal Dual-Stream Neural Network for Sequential Whole-Body PET Segmentation [10.344707825773252]
  We propose a 'dual-stream' neural network (ST-DSNN) to segment sequential whole-body PET scans. Our ST-DSNN learns and accumulates image features from the PET images done over time. Our results show that our method outperforms the state-of-the-art PET image segmentation methods.
  arXiv  Detail & Related papers  (2021-06-09T10:15:20Z)
• Evidential segmentation of 3D PET/CT images [20.65495780362289]
  A segmentation method based on belief functions is proposed to segment lymphomas in 3D PET/CT images. The architecture is composed of a feature extraction module and an evidential segmentation (ES) module. The method was evaluated on a database of 173 patients with diffuse large b-cell lymphoma.
  arXiv  Detail & Related papers  (2021-04-27T16:06:27Z)
• Combining CNN and Hybrid Active Contours for Head and Neck Tumor Segmentation in CT and PET images [16.76087435628378]
  We propose an automatic segmentation method for head and neck tumors based on the combination of convolutional neural networks (CNNs) and hybrid active contours. Our method ranked second place in the MICCAI 2020 HECKTOR challenge with average Dice Similarity Coefficient, precision, and recall of 0.752, 0.838, and 0.717, respectively.
  arXiv  Detail & Related papers  (2020-12-28T12:12:14Z)
• Multimodal Spatial Attention Module for Targeting Multimodal PET-CT Lung Tumor Segmentation [11.622615048002567]
  Multimodal spatial attention module (MSAM) learns to emphasize regions related to tumors. MSAM can be applied to common backbone architectures and trained end-to-end.
  arXiv  Detail & Related papers  (2020-07-29T10:27:22Z)
• Spectral-Spatial Recurrent-Convolutional Networks for In-Vivo Hyperspectral Tumor Type Classification [49.32653090178743]
  We demonstrate the feasibility of in-vivo tumor type classification using hyperspectral imaging and deep learning. Our best model achieves an AUC of 76.3%, significantly outperforming previous conventional and deep learning methods.
  arXiv  Detail & Related papers  (2020-07-02T12:00:53Z)

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.