Neurofibromatosis Tumor Segmentation on Whole-body MRI
Creators: name: Wenli Cai
name: Dombi, Eva
name: Ly, Ina
name: Guibo Luo
name: Allaway, Robert
name: Li, Yin
name: Ding, Ke
name: Chen, Xin
Publisher: name: Zenodo
Year: 2023
Subjects: Whole-body MRI
Neurofibromatosis Segmentation
Neurofibromatosis Detection
Whole-body Tumor Burden
MICCAI Challenges

description: Whole-Body MRI (WBMRI) has been available for clinical use since the early 2000’s, providing a non-ionizing method for screening, diagnosis, staging, and treatment evaluation in pediatric and adult patients with oncologic and nononcologic abnormalities (such as genetic cancer, malignancy, inflammatory lesions, or other disorders). However, the image analysis techniques on WBMRI are still in its early stages. Neurofibromatosis (NF), including NF1, NF2, and schwannomatosis, is a group of genetic disorders that cause tumors to grow on nerve tissues. NFs can occur anywhere in the body from a handful of tumors to hundreds of tumors and are broadly classified as discrete (involving a single nerve) or plexiform (involving multiple nerve fascicles). Plexiform neurofibromas tend to have a diffuse and infiltrative architecture which is clinically unmeasurable by conventional linear measurement. WBMRI has emerged as the standard non-invasive imaging modality for monitoring of longitudinal NF tumor size changes, early detection of NF1-related malignancy, and evaluation of treatment response in NF-related clinical care and clinical trials. Volumetric quantification of NF tumor burden on WBMRI including the number, size (volume), location (body part), and type (discrete or plexiform) of tumors carried by an NF patient has been widely recognized as an objective imaging biomarker for evaluation of disease status and assessment of treatment response. Accurate detection and segmentation of NF tumors on WBMRI are essential for volumetric quantification of NF tumor burden. However, existing automated tumor segmentation methods are insufficient for NF tumors, due to the lack of large-scale benchmarks, as well as the varying shape and size of tumors in high resolution WBMRI data. The current state of clinical practice hence relies on manual or semi-automated segmentation methods for volumetric quantification of NF on WBMRI, which is very labor intensive and time-consuming. It may take several hours for quantification of complex cases. This has become a hurdle for physicians using WBMRI in treatment decision- making of NF patients, and is a particularly major challenge in running clinical trials that rely on longitudinal of NF tumors. As a result, it is urgent to develop high-accuracy segmentation methods for satisfying clinical requirements. The technical challenges for automated segmentation of NF tumors on WBMRI are their high variation in tumor location, size, shape, infiltration, and heterogeneity, particularly in plexiform neurofibromas. Our results show that the widely-adopted U-Net model and its variants (such as nnU-Net, UNet++, etc.) exhibit significantly lower accuracy in the segmentation of NFs on WBMRI, when compared to other MRI segmentation tasks. To address these technical challenges for automated volumetric quantification of NFs on WBMRI, we are developing this benchmarking Challenge to identify novel solutions of deep-learning based segmentation algorithms, with the long-term goal of fostering a research community focused on WBMRI image analysis techniques. Furthermore, there is no publicly available WBMRI-NF dataset with labeled tumors and whole-body tumor burden reports. Therefore, we will host the first WBMRI-NF Challenge in MICCAI 2023, which will provide 400 WBMRI scans (&gt;2000 NF tumors) collected from our clinical working protocol of NF volumetric quantification on WBMRI, each with radiologist-confirmed segmentation masks and volumetric tumor-burden reports. The dataset will be split into 200 for training, 50 for open validation, and 150 for the closed testing. This will build a robust benchmark for the medical imaging community to investigate novel automated deep-learning solutions for volumetric quantification of NF tumor burden on WBMRI. The WBMRI-NF 2023 Challenge tasks participants to develop automated segmentation models of NF tumors on WBMRI. The models are expected to segment and detect NF tumors by estimating one volumetric mask for each tumor. The model performance will be evaluated in terms of segmentation masks and volumetric tumor volume of closed test dataset using both segmentation and detection metrics. Statistical analysis will be performed to confirm the ranking of the results. This Challenge will promote image analysis techniques on WBMRI, particularly detection and segmentation methods for highly irregular and infiltrative tumors like NFs, improve the clinical care of NF patients and the clinical trial evaluation of NF drugs, bring more attention and innovative treatments for rare disease like NF, and bridge interdisciplinary communication between researchers and practitioners of medical image analysis, computer vision, and machine learning, to investigate novel solutions to overcome the technical barriers. We will also organize a half-day challenge meeting for top-performing teams to present their results and discuss open technical and clinical challenges related to volumetric quantification of NF on WBMRI.