最近,nnUNet V2的发布进一步推动了这一技术的发展。本文将介绍nnUNet V2相较于其前一代版本的主要改进,并为您提供一个简单易懂的安装指南。nnUNet V2的主要改进:名称更改:为了消除对医学图像的强烈偏见,modality在V2版本中被重命名为channel_names。这有助于更好地理解和处理多模态医学图像数据。标签结构变化:在V2...
{"channel_names":{"0":"CT"},"labels":{"background":0,"kidney":[1,2,3],"masses":[2,3],"tumor":2},"numTraining":489,"file_ending":".nii.gz","name":"KiTS2023","reference":"none","release":"prerelease","description":"KiTS2023","overwrite_image_reader_writer":"NibabelIOWith...
Channel_names确定 nnU-Net 使用的归一化。如果通道被标记为“CT”,则将使用基于前景像素强度的全局归一化。如果是其他情况,将使用 per-channel z-scoring 相对于 nnU-Net v1 的重要变化: modality现在称为channel_names,以消除对医学图像的强烈偏见 Lable 的结构不同(name -> int 而不是 int -> name)。这样...
Channel_names确定 nnU-Net 使用的归一化。如果通道被标记为“CT”,则将使用基于前景像素强度的全局归一化。如果是其他情况,将使用 per-channel z-scoring 相对于 nnU-Net v1 的重要变化: [图片上传失败...(image-4451cb-1693737704302)] modality现在称为channel_names,以消除对医学图像的强烈偏见 Lable 的结构...
"channel_names": { "0":"CT"}, "labels": { "background":0,"sign 51":1, ··· }, "folds":1,"numTraining":254,"file_ending":".nii.gz","window_width":1600,"window_level": -600,"overwrite_image_reader_writer":"NibabelIOWithReorient"} ...
"channel_names": { # 模态/通道名, nnUNet 其实只关心它是不是CT,因为CT 的归一化方式不一样。 "0": "T1", "1": "T2" }, "labels": { # 这里的分割图上的类别名 "background": 0, "PZ": 1, "TZ": 2 }, "numTraining": 32, ...
{ "channel_names": { "0": "CT" }, "labels": { "background": 0, "kidney": [ 1, 2, 3 ], "masses": [ 2, 3 ], "tumor": 2 }, "numTraining": 489, "file_ending": ".nii.gz", "name": "KiTS2023", "reference": "none", "release": "prerelease", "description": "...
数据集应放置于nnUNet_raw文件夹中。与V1版本相比，数据集命名有所调整，TaskXXXX更改为DatasetXXX。构建dataset.json文件时，请注意版本2的更新。该文件包含用于nnU-Net训练的元数据，自版本1起，必填字段数量显著减少。以MSD Dataset005_Prostate为例，Channel_names确定归一化方式，CT标记的通道将采用...
(out_dir), channel_names={ 0: "cineMRI", }, labels={ "background": 0, "RV": 1, "MLV": 2, "LVC": 3, }, file_ending=".nii.gz", num_training_cases=num_training_cases, ) if __name__ == "__main__": import argparse parser = argparse.ArgumentParser() parser.add_argument...
Reminder: The channel_names entry typically looks like this: "channel_names": { "0": "T2", "1": "ADC" }, It has as many entries as there are input channels for the given dataset. To tell you a secret, nnU-Net does not really care what your channels are called. We just use ...