QCResUNet: Joint Subject-Level and Voxel-Level Prediction of Segmentation Quality

Peijie Qiu, Satrajit Chakrabarty, Phuc Nguyen, Soumyendu Sekhar Ghosh, Aristeidis Sotiras

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Deep learning has achieved state-of-the-art performance in automated brain tumor segmentation from magnetic resonance imaging (MRI) scans. However, the unexpected occurrence of poor-quality outliers, especially in out-of-distribution samples, hinders their translation into patient-centered clinical practice. Therefore, it is important to develop automated tools for large-scale segmentation quality control (QC). However, most existing QC methods targeted cardiac MRI segmentation which involves a single modality and a single tissue type. Importantly, these methods only provide a subject-level segmentation-quality prediction, which cannot inform clinicians where the segmentation needs to be refined. To address this gap, we proposed a novel network architecture called QCResUNet that simultaneously produces segmentation-quality measures as well as voxel-level segmentation error maps for brain tumor segmentation QC. To train the proposed model, we created a wide variety of segmentation-quality results by using i) models that have been trained for a varying number of epochs with different modalities; and ii) a newly devised segmentation-generation method called SegGen. The proposed method was validated on a large public brain tumor dataset with segmentations generated by different methods, achieving high performance on the prediction of segmentation-quality metric as well as voxel-wise localization of segmentation errors. The implementation will be publicly available at https://github.com/peijie-chiu/QC-ResUNet.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings
EditorsHayit Greenspan, Hayit Greenspan, Anant Madabhushi, Parvin Mousavi, Septimiu Salcudean, James Duncan, Tanveer Syeda-Mahmood, Russell Taylor
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages10
ISBN (Print)9783031439001
StatePublished - 2023
Event26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023 - Vancouver, Canada
Duration: Oct 8 2023Oct 12 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14223 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023


  • Automatic quality control
  • Brain tumor segmentation
  • Deep Learning


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