Purpose To develop a deep learning model for multi-anatomy and many-class
segmentation of diverse anatomic structures on MRI imaging.
Materials and Methods In this retrospective study, two datasets were curated
and annotated for model development and evaluation. An internal dataset of 1022
MRI sequences from various clinical sites within a health system and an
external dataset of 264 MRI sequences from an independent imaging center were
collected. In both datasets, 49 anatomic structures were annotated as the
ground truth. The internal dataset was divided into training, validation, and
test sets and used to train and evaluate an nnU-Net model. The external dataset
was used to evaluate nnU-Net model generalizability and performance in all
classes on independent imaging data. Dice scores were calculated to evaluate
model segmentation performance.
Results The model achieved an average Dice score of 0.801 on the internal
test set, and an average score of 0.814 on the complete external dataset across
49 classes.
Conclusion The developed model achieves robust and generalizable segmentation
of 49 anatomic structures on MRI imaging. A future direction is focused on the
incorporation of additional anatomic regions and structures into the datasets
and model.

通过开发深度学习模型，实现 MRI 图像上多解剖结构和众多类别的分割。

MRAnnotator: 一个用于 MRI 分割的多解剖结构深度学习模型

MRAnnotator: A Multi-Anatomy Deep Learning Model for MRI Segmentation

Deep learning (DL) based contrast dose reduction and elimination in MRI
imaging is gaining traction, given the detrimental effects of Gadolinium-based
Contrast Agents (GBCAs). These DL algorithms are however limited by the
availability of high quality low dose datasets. Additionally, different types
of GBCAs and pathologies require different dose levels for the DL algorithms to
work reliably. In this work, we formulate a novel transformer (Gformer) based
iterative modelling approach for the synthesis of images with arbitrary
contrast enhancement that corresponds to different dose levels. The proposed
Gformer incorporates a sub-sampling based attention mechanism and a rotational
shift module that captures the various contrast related features. Quantitative
evaluation indicates that the proposed model performs better than other
state-of-the-art methods. We further perform quantitative evaluation on
downstream tasks such as dose reduction and tumor segmentation to demonstrate
the clinical utility.

基于深度学习的磁共振成像对比剂剂量降低和消除正受到关注，限制于高质量低剂量数据集的可用性导致这些深度学习算法受到限制。本研究提出了一种基于转换器 (Gformer) 的新型迭代建模方法，用于合成具有任意对比度增强的图像，从而对应于不同的剂量水平。Gformer 模型结合了基于子采样的注意机制和旋转位移模块，捕捉各种对比度相关特征。定量评估表明，所提出的模型性能优于其他现有方法。进一步，在剂量减少和肿瘤分割等下游任务上开展了定量评估以证明其临床实用性。

MRI 使用迭代全局变换模型模拟任意级别对比剂剂量

Simulation of Arbitrary Level Contrast Dose in MRI Using an Iterative  Global Transformer Model

Semantic segmentation of brain tumors is a fundamental medical image analysis
task involving multiple MRI imaging modalities that can assist clinicians in
diagnosing the patient and successively studying the progression of the
malignant entity. In recent years, Fully Convolutional Neural Networks (FCNNs)
approaches have become the de facto standard for 3D medical image segmentation.
The popular "U-shaped" network architecture has achieved state-of-the-art
performance benchmarks on different 2D and 3D semantic segmentation tasks and
across various imaging modalities. However, due to the limited kernel size of
convolution layers in FCNNs, their performance of modeling long-range
information is sub-optimal, and this can lead to deficiencies in the
segmentation of tumors with variable sizes. On the other hand, transformer
models have demonstrated excellent capabilities in capturing such long-range
information in multiple domains, including natural language processing and
computer vision. Inspired by the success of vision transformers and their
variants, we propose a novel segmentation model termed Swin UNEt TRansformers
(Swin UNETR). Specifically, the task of 3D brain tumor semantic segmentation is
reformulated as a sequence to sequence prediction problem wherein multi-modal
input data is projected into a 1D sequence of embedding and used as an input to
a hierarchical Swin transformer as the encoder. The swin transformer encoder
extracts features at five different resolutions by utilizing shifted windows
for computing self-attention and is connected to an FCNN-based decoder at each
resolution via skip connections. We have participated in BraTS 2021
segmentation challenge, and our proposed model ranks among the top-performing
approaches in the validation phase. Code: this https URL

利用 Swint UNEt TRansformers 模型和多模 MRI 数据对 3D 颅脑肿瘤进行语义分割，可以转换为序列预测问题，以 5 个不同分辨率提取特征，通过跳过连接连接到 FCNN 解码器，并在 BraTS 2021 分割挑战中表现出优越的性能。