QCResUNet: Joint Subject-level and Voxel-level Segmentation Quality Prediction

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

作者机构：Mallinckrodt Institute of Radiology Washington University School of Medicine St. LouisMO United States Department of Electrical and Systems Engineering Washington University in St. Louis St. LouisMO United States Department of Biomedical Engineering University of Cincinnati CincinnatiOH United States Institute for Informatics Data Science & Biostatistics Washington University School of Medicine St. LouisMO United States 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2024年

核心收录：

主　　题：Magnetic resonance imaging 

摘      要：Deep learning has made significant strides in automated brain tumor segmentation from magnetic resonance imaging (MRI) scans in recent years. However, the reliability of these tools is hampered by the presence of poor-quality segmentation outliers, particularly in out-of-distribution samples, making their implementation in clinical practice difficult. Therefore, there is a need for quality control (QC) to screen the quality of the segmentation results. Although numerous automatic QC methods have been developed for segmentation quality screening, most were designed for cardiac MRI segmentation, which involves a single modality and a single tissue type. Furthermore, most prior works only provided subject-level predictions of segmentation quality and did not identify erroneous parts segmentation that may require refinement. To address these limitations, we proposed a novel multi-task deep learning architecture, termed QCResUNet, which produces subject-level segmentation-quality measures as well as voxel-level segmentation error maps for each available tissue class. To validate the effectiveness of the proposed method, we conducted experiments on assessing its performance on evaluating the quality of two distinct segmentation tasks. First, we aimed to assess the quality of brain tumor segmentation results. For this task, we performed experiments on one internal (Brain Tumor Segmentation (BraTS) Challenge 2021, n = 1, 251) and two external datasets (BraTS Challenge 2023 in Sub-Saharan Africa Patient Population (BraTS-SSA), n = 40;Washington University School of Medicine (WUSM), n = 175). Specifically, we first performed a three-fold cross-validation on the internal dataset using segmentations generated by different methods at various quality levels, followed by an evaluation on the external datasets. Second, we aimed to evaluate the segmentation quality of cardiac Magnetic Resonance Imaging (MRI) data from the Automated Cardiac Diagnosis Challenge (ACDC, n = 100). The pr