Brain tumors are the most common solid tumors and the leading cause of
cancer-related death among children. Tumor segmentation is essential in
surgical and treatment planning, and response assessment and monitoring.
However, manual segmentation is time-consuming and has high inter-operator
variability, underscoring the need for more efficient methods. We compared two
deep learning-based 3D segmentation models, DeepMedic and nnU-Net, after
training with pediatric-specific multi-institutional brain tumor data using
based on multi-parametric MRI scans.Multi-parametric preoperative MRI scans of
339 pediatric patients (n=293 internal and n=46 external cohorts) with a
variety of tumor subtypes, were preprocessed and manually segmented into four
tumor subregions, i.e., enhancing tumor (ET), non-enhancing tumor (NET), cystic
components (CC), and peritumoral edema (ED). After training, performance of the
two models on internal and external test sets was evaluated using Dice scores,
sensitivity, and Hausdorff distance with reference to ground truth manual
segmentations. Dice score for nnU-Net internal test sets was (mean +/- SD
(median)) 0.9+/-0.07 (0.94) for WT, 0.77+/-0.29 for ET, 0.66+/-0.32 for NET,
0.71+/-0.33 for CC, and 0.71+/-0.40 for ED, respectively. For DeepMedic the
Dice scores were 0.82+/-0.16 for WT, 0.66+/-0.32 for ET, 0.48+/-0.27, for NET,
0.48+/-0.36 for CC, and 0.19+/-0.33 for ED, respectively. Dice scores were
significantly higher for nnU-Net (p<=0.01). External validation of the trained
nnU-Net model on the multi-institutional BraTS-PEDs 2023 dataset revealed high
generalization capability in segmentation of whole tumor and tumor core with
Dice scores of 0.87+/-0.13 (0.91) and 0.83+/-0.18 (0.89), respectively.
Pediatric-specific data trained nnU-Net model is superior to DeepMedic for
whole tumor and subregion segmentation of pediatric brain tumors.

通过多机构儿童专用多参 MRI 扫描数据训练，我们比较了两种基于深度学习的三维分割模型，即 DeepMedic 和 nnU-Net，在脑肿瘤分割方面的性能，结果显示儿童专用数据训练的 nnU-Net 模型在整个肿瘤及亚区域分割方面优于 DeepMedic。

训练和比较 nnU-Net 和 DeepMedic 方法用于儿童脑肿瘤自动分割

Training and Comparison of nnU-Net and DeepMedic Methods for  Autosegmentation of Pediatric Brain Tumors

Automated segmentation of distinct tumor regions is critical for accurate
diagnosis and treatment planning in pediatric brain tumors. This study
evaluates the efficacy of the Multi-Planner U-Net (MPUnet) approach in
segmenting different tumor subregions across three challenging datasets:
Pediatrics Tumor Challenge (PED), Brain Metastasis Challenge (MET), and
Sub-Sahara-Africa Adult Glioma (SSA). These datasets represent diverse
scenarios and anatomical variations, making them suitable for assessing the
robustness and generalization capabilities of the MPUnet model. By utilizing
multi-planar information, the MPUnet architecture aims to enhance segmentation
accuracy. Our results show varying performance levels across the evaluated
challenges, with the tumor core (TC) class demonstrating relatively higher
segmentation accuracy. However, variability is observed in the segmentation of
other classes, such as the edema and enhancing tumor (ET) regions. These
findings emphasize the complexity of brain tumor segmentation and highlight the
potential for further refinement of the MPUnet approach and inclusion of MRI
more data and preprocessing.

MPUnet 在分割儿童脑肿瘤的不同区域方面显示出差异性能水平，其中肿瘤核心（TC）类表现出相对较高的分割准确性，然而在水肿和增强肿瘤（ET）区域的分割中观察到变异，这凸显了脑肿瘤分割的复杂性并强调了进一步完善 MPUnet 方法和包括 MRI 更多数据和预处理的潜力。

基于多平面 UNet 的全自动脑 MRI 数据肿瘤分割

Fully Automated Tumor Segmentation for Brain MRI data using Multiplanner  UNet

Pediatric tumors of the central nervous system are the most common cause of
cancer-related death in children. The five-year survival rate for high-grade
gliomas in children is less than 20\%. Due to their rarity, the diagnosis of
these entities is often delayed, their treatment is mainly based on historic
treatment concepts, and clinical trials require multi-institutional
collaborations. The MICCAI Brain Tumor Segmentation (BraTS) Challenge is a
landmark community benchmark event with a successful history of 12 years of
resource creation for the segmentation and analysis of adult glioma. Here we
present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge, which
represents the first BraTS challenge focused on pediatric brain tumors with
data acquired across multiple international consortia dedicated to pediatric
neuro-oncology and clinical trials. The BraTS-PEDs 2023 challenge focuses on
benchmarking the development of volumentric segmentation algorithms for
pediatric brain glioma through standardized quantitative performance evaluation
metrics utilized across the BraTS 2023 cluster of challenges. Models gaining
knowledge from the BraTS-PEDs multi-parametric structural MRI (mpMRI) training
data will be evaluated on separate validation and unseen test mpMRI dataof
high-grade pediatric glioma. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023
challenge brings together clinicians and AI/imaging scientists to lead to
faster development of automated segmentation techniques that could benefit
clinical trials, and ultimately the care of children with brain tumors.

CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge is the first BraTS challenge focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials, aiming to develop volumentric segmentation algorithms for high-grade pediatric glioma utilizing standardized quantitative performance evaluation metrics and multi-parametric structural MRI (mpMRI) training data.