TY  - JOUR
AB  - Accurate assessment of lymph node size in 3D CT scans is crucial for 
      cancer staging, therapeutic management, and monitoring treatment 
      response. Existing state-of-the-art segmentation frameworks in medical 
      imaging often rely on fully annotated datasets. However, for lymph node 
      segmentation, these datasets are typically small due to the extensive 
      time and expertise required to annotate the numerous lymph nodes in 3D 
      CT scans. Weakly-supervised learning, which leverages incomplete or 
      noisy annotations, has recently gained interest in the medical imaging 
      community as a potential solution. Despite the variety of 
      weakly-supervised techniques proposed, most have been validated only on 
      private datasets or small publicly available datasets. To address this 
      limitation, the Mediastinal Lymph Node Quantification (LNQ) challenge 
      was organized in conjunction with the 26th International Conference on 
      Medical Image Computing and Computer Assisted Intervention (MICCAI 
      2023). This challenge aimed to advance weakly-supervised segmentation 
      methods by providing a new, partially annotated dataset and a robust 
      evaluation framework. A total of 16 teams from 5 countries submitted 
      predictions to the validation leaderboard, and 6 teams from 3 countries 
      participated in the evaluation phase. The results highlighted both the 
      potential and the current limitations of weakly-supervised approaches. 
      On one hand, weakly-supervised approaches obtained relatively good 
      performance with a median Dice score of 61.0%. On the other hand, 
      top-ranked teams, with a median Dice score exceeding 70%, boosted their 
      performance by leveraging smaller but fully annotated datasets to 
      combine weak supervision and full supervision. This highlights both the 
      promise of weakly-supervised methods and the ongoing need for 
      high-quality, fully annotated data to achieve higher segmentation 
      performance.
AU  - Dorent, R.*
AU  - Khajavi, R.*
AU  - Idris, T.*
AU  - Ziegler, E.*
AU  - Somarouthu, B.*
AU  - Jacene, H.A.*
AU  - LaCasce, A.S.*
AU  - Deissler, J.*
AU  - Ehrhardt, J.*
AU  - Engelson, S.*
AU  - Fischer, S.M.
AU  - Gu, Y.*
AU  - Handels, H.*
AU  - Kasai, S.*
AU  - Kondo, S.*
AU  - Maier‐Hein, K.*
AU  - Schnabel, J.A.
AU  - Wang, G.*
AU  - Wang, L.*
AU  - Wald, T.*
AU  - Yang, G.*
AU  - Zhang, H.*
AU  - Zhang, M.*
AU  - Pieper, S.*
AU  - Harris, G.J.*
AU  - Kikinis, R.*
AU  - Kapur, T.*
C1  - 74763
C2  - 57676
SP  - 1 - 15
TI  - LNQ 2023 challenge: Benchmark of weakly-supervised techniques for mediastinal lymph node quantification.
JO  - MELBA
VL  - 3
IS  - MICCAI 2023 LNQ challenge
PY  - 2025
SN  - 2766-905X
ER  - 

TY  - JOUR
AB  - Organ segmentation in CT volumes is an important pre-processing step in many computerassisted intervention and diagnosis methods.  In recent years, convolutional neural networkshave dominated the state of the art in this task.  However, since this problem presents achallenging environment due to high variability in the organ’s shape and similarity betweentissues, the generation of false negative and false positive regions in the output segmentationis a common issue.  Recent works have shown that the uncertainty analysis of the modelcan provide us with useful information about potential errors in the segmentation.  In thiscontext,  we  proposed  a  segmentation  refinement  method  based  on  uncertainty  analysisand graph convolutional networks.  We employ the uncertainty levels of the convolutionalnetwork in a particular input volume to formulate a semi-supervised graph learning problemthat is solved by training a graph convolutional network.  To test our method we refine theinitial output of a 2D U-Net.  We validate our framework with the NIH pancreas datasetand the spleen dataset of the medical segmentation decathlon.  We show that our methodoutperforms the state-of-the-art CRF refinement method by improving the dice score by1%  for  the  pancreas  and  2%  for  spleen,  with  respect  to  the  original  U-Net’s  prediction.Finally,  we perform a sensitivity analysis on the parameters of our proposal and discussthe  applicability  to  other  CNN  architectures,  the  results,  and  current  limitations  of  themodel for future work in this research direction.  For reproducibility purposes, we make ourcode publicly available athttps://github.com/rodsom22/gcn_refinement.
AU  - Soberanis-Mukul, R.D.*
AU  - Navab, N.*
AU  - Albarqouni, S.
C1  - 61561
C2  - 49916
SP  - 1-27
TI  - An uncertainty-driven GCN refinement strategy for organ segmentation.
JO  - MELBA
VL  - 1
IS  - 1
PY  - 2020
SN  - 2766-905X
ER  -