Weakly Supervised Lesion Co-segmentation on CT Scans

• URL: http://arxiv.org/abs/2001.09174v1
• Date: Fri, 24 Jan 2020 19:39:33 GMT
• Title: Weakly Supervised Lesion Co-segmentation on CT Scans
• Authors: Vatsal Agarwal, Youbao Tang, Jing Xiao, Ronald M. Summers
• Abstract summary: We propose a weakly-supervised co-segmentation model that first generates pseudo-masks from the RECIST slices. We then use these as training labels for an attention-based convolutional neural network capable of segmenting common lesions from a pair of CT scans.
• Score: 18.58056402884405
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Lesion segmentation in medical imaging serves as an effective tool for assessing tumor sizes and monitoring changes in growth. However, not only is manual lesion segmentation time-consuming, but it is also expensive and requires expert radiologist knowledge. Therefore many hospitals rely on a loose substitute called response evaluation criteria in solid tumors (RECIST). Although these annotations are far from precise, they are widely used throughout hospitals and are found in their picture archiving and communication systems (PACS). Therefore, these annotations have the potential to serve as a robust yet challenging means of weak supervision for training full lesion segmentation models. In this work, we propose a weakly-supervised co-segmentation model that first generates pseudo-masks from the RECIST slices and uses these as training labels for an attention-based convolutional neural network capable of segmenting common lesions from a pair of CT scans. To validate and test the model, we utilize the DeepLesion dataset, an extensive CT-scan lesion dataset that contains 32,735 PACS bookmarked images. Extensive experimental results demonstrate the efficacy of our co-segmentation approach for lesion segmentation with a mean Dice coefficient of 90.3%.

Related papers

• Weakly-Supervised Detection of Bone Lesions in CT [48.34559062736031]
  The skeletal region is one of the common sites of metastatic spread of cancer in the breast and prostate. We developed a pipeline to detect bone lesions in CT volumes via a proxy segmentation task. Our method detected bone lesions in CT with a precision of 96.7% and recall of 47.3% despite the use of incomplete and partial training data.
  arXiv  Detail & Related papers  (2024-01-31T21:05:34Z)
• Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective [51.70661197256033]
  We propose ARCO, a semi-supervised contrastive learning framework with stratified group theory for medical image segmentation. We first propose building ARCO through the concept of variance-reduced estimation and show that certain variance-reduction techniques are particularly beneficial in pixel/voxel-level segmentation tasks. We experimentally validate our approaches on eight benchmarks, i.e., five 2D/3D medical and three semantic segmentation datasets, with different label settings.
  arXiv  Detail & Related papers  (2023-02-03T13:50:25Z)
• Reliable Joint Segmentation of Retinal Edema Lesions in OCT Images [55.83984261827332]
  In this paper, we propose a novel reliable multi-scale wavelet-enhanced transformer network. We develop a novel segmentation backbone that integrates a wavelet-enhanced feature extractor network and a multi-scale transformer module. Our proposed method achieves better segmentation accuracy with a high degree of reliability as compared to other state-of-the-art segmentation approaches.
  arXiv  Detail & Related papers  (2022-12-01T07:32:56Z)
• Generative Adversarial Networks for Weakly Supervised Generation and Evaluation of Brain Tumor Segmentations on MR Images [0.0]
  This work presents a weakly supervised approach to segment anomalies in 2D magnetic resonance images. We train a generative adversarial network (GAN) that converts cancerous images to healthy variants. Non-cancerous variants can also be used to evaluate the segmentations in a weakly supervised fashion.
  arXiv  Detail & Related papers  (2022-11-10T00:04:46Z)
• Whole-Body Lesion Segmentation in 18F-FDG PET/CT [11.662584140924725]
  The proposed model is designed on the basis of the joint 2D and 3D nnUNET architecture to predict lesions across the whole body. We evaluate the proposed method in the context of AutoPet Challenge, which measures the lesion segmentation performance in the metrics of dice score, false-positive volume and false-negative volume.
  arXiv  Detail & Related papers  (2022-09-16T10:49:53Z)
• Mixed-UNet: Refined Class Activation Mapping for Weakly-Supervised Semantic Segmentation with Multi-scale Inference [28.409679398886304]
  We develop a novel model named Mixed-UNet, which has two parallel branches in the decoding phase. We evaluate the designed Mixed-UNet against several prevalent deep learning-based segmentation approaches on our dataset collected from the local hospital and public datasets.
  arXiv  Detail & Related papers  (2022-05-06T08:37:02Z)
• Weakly-Supervised Universal Lesion Segmentation with Regional Level Set Loss [16.80758525711538]
  We present a novel weakly-supervised universal lesion segmentation method based on the High-Resolution Network (HRNet) AHRNet provides advanced high-resolution deep image features by involving a decoder, dual-attention and scale attention mechanisms. Our method achieves the best performance on the publicly large-scale DeepLesion dataset and a hold-out test set.
  arXiv  Detail & Related papers  (2021-05-03T23:33:37Z)
• Synergistic Learning of Lung Lobe Segmentation and Hierarchical Multi-Instance Classification for Automated Severity Assessment of COVID-19 in CT Images [61.862364277007934]
  We propose a synergistic learning framework for automated severity assessment of COVID-19 in 3D CT images. A multi-task deep network (called M$2$UNet) is then developed to assess the severity of COVID-19 patients. Our M$2$UNet consists of a patch-level encoder, a segmentation sub-network for lung lobe segmentation, and a classification sub-network for severity assessment.
  arXiv  Detail & Related papers  (2020-05-08T03:16:15Z)
• A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging [90.29017019187282]
  " 2018 Left Atrium Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset. Analyse of the submitted algorithms using technical and biological metrics was performed. Results show the top method achieved a dice score of 93.2% and a mean surface to a surface distance of 0.7 mm.
  arXiv  Detail & Related papers  (2020-04-26T08:49:17Z)
• Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images [152.34988415258988]
  Automated detection of lung infections from computed tomography (CT) images offers a great potential to augment the traditional healthcare strategy for tackling COVID-19. segmenting infected regions from CT slices faces several challenges, including high variation in infection characteristics, and low intensity contrast between infections and normal tissues. To address these challenges, a novel COVID-19 Deep Lung Infection Network (Inf-Net) is proposed to automatically identify infected regions from chest CT slices.
  arXiv  Detail & Related papers  (2020-04-22T07:30:56Z)
• Weakly-Supervised Lesion Segmentation on CT Scans using Co-Segmentation [18.58056402884405]
  Lesion segmentation on computed tomography (CT) scans is an important step for precisely monitoring changes in lesion/tumor growth. Current practices rely on an imprecise substitute called response evaluation criteria in solid tumors. This paper proposes a convolutional neural network based weakly-supervised lesion segmentation method.
  arXiv  Detail & Related papers  (2020-01-23T15:15:53Z)

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.