mlsanityData to 7 Oct 2026

Lesion Segmentation and RECIST Diameter Prediction via Click-driven Attention and Dual-path Connection

arXiv 2105.01828 ↗First posted 5 May 2021eess.IV cs.CVcited by 8

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Abstract (arXiv)

Measuring lesion size is an important step to assess tumor growth and monitor disease progression and therapy response in oncology image analysis. Although it is tedious and highly time-consuming, radiologists have to work on this task by using RECIST criteria (Response Evaluation Criteria In Solid Tumors) routinely and manually. Even though lesion segmentation may be the more accurate and clinically more valuable means, physicians can not manually segment lesions as now since much more heavy laboring will be required. In this paper, we present a prior-guided dual-path network (PDNet) to segment common types of lesions throughout the whole body and predict their RECIST diameters accurately and automatically. Similar to [1], a click guidance from radiologists is the only requirement. There are two key characteristics in PDNet: 1) Learning lesion-specific attention matrices in parallel from the click prior information by the proposed prior encoder, named click-driven attention; 2) Aggregating the extracted multi-scale features comprehensively by introducing top-down and bottom-up connections in the proposed decoder, named dual-path connection. Experiments show the superiority of our proposed PDNet in lesion segmentation and RECIST diameter prediction using the DeepLesion dataset and an external test set. PDNet learns comprehensive and representative deep image features for our tasks and produces more accurate results on both lesion segmentation and RECIST diameter prediction.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 26 of its 40 references that are in the map. First posted on arXiv 5 May 2021.

Builds on

26 of 40 references.

References · 26

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14 more
  1. 2019MULAN: Multitask Universal Lesion Analysis Network for Joint Lesion Detection, Tagging, and Segmentationcited by 39
  2. 2018CT-Realistic Lung Nodule Simulation from 3D Conditional Generative Adversarial Networks for Robust Lung Segmentationcited by 34
  3. 2020Deep Lesion Tracker: Monitoring Lesions in 4D Longitudinal Imaging Studiescited by 25
  4. 2019ULDor: A Universal Lesion Detector for CT Scans with Pseudo Masks and Hard Negative Example Miningcited by 21
  5. 2018Accurate Weakly-Supervised Deep Lesion Segmentation using Large-Scale Clinical Annotations: Slice-Propagated 3D Mask Generation from 2D RECISTcited by 17
  6. 2021Weakly-Supervised Universal Lesion Segmentation with Regional Level Set Losscited by 16
  7. 2020Lymph Node Gross Tumor Volume Detection and Segmentation via Distance-based Gating using 3D CT/PET Imaging in Radiotherapycited by 15
  8. 2020E2Net: An Edge Enhanced Network for Accurate Liver and Tumor Segmentation on CT Scanscited by 12
  9. 2020One Click Lesion RECIST Measurement and Segmentation on CT Scanscited by 10
  10. 2020Knowledge Distillation with Adaptive Asymmetric Label Sharpening for Semi-supervised Fracture Detection in Chest X-rayscited by 9
  11. 2020Weakly Supervised Lesion Co-segmentation on CT Scanscited by 7
  12. 2018Semi-Automatic RECIST Labeling on CT Scans with Cascaded Convolutional Neural Networkscited by 5
  13. 2018CT Image Enhancement Using Stacked Generative Adversarial Networks and Transfer Learning for Lesion Segmentation Improvementcited by 5
  14. 2020Weakly-Supervised Lesion Segmentation on CT Scans using Co-Segmentationcited by 5

Built on by

Cited by year

Citing papers per year: 2022: 4; 2023: 2; 2024: 2.

8 papers.

  1. 2024The ULS23 Challenge: a Baseline Model and Benchmark Dataset for 3D Universal Lesion Segmentation in Computed Tomographycited by 26
  2. 2022BiometryNet: Landmark-based Fetal Biometry Estimation from Standard Ultrasound Planescited by 9
  3. 2022Accurate and Robust Lesion RECIST Diameter Prediction and Segmentation with Transformerscited by 4
  4. 2023Scale-aware Test-time Click Adaptation for Pulmonary Nodule and Mass Segmentationcited by 3
  5. 2023RECIST Weakly Supervised Lesion Segmentation via Label-Space Co-Trainingcited by 2
  6. 2022AGMR-Net: Attention Guided Multiscale Recovery framework for stroke segmentationcited by 1
  7. 2024Optimizing Universal Lesion Segmentation: State Space Model-Guided Hierarchical Networks with Feature Importance Adjustmentcited by 1
  8. 2022Exploiting segmentation labels and representation learning to forecast therapy response of PDAC patientscited by 0