Comments (25)
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预处理后的HCP-1200数据链接在这里:https://drive.google.com/file/d/1xAAAPM3QBM6xxswdrVHb8auyECNrVdvz/view?usp=sharing
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由于测试集中是完整的图像,而非训练集和验证集中的patch。因此,测试集体积较大(222个图像,GT图像共计5GB)。这里我刚上传了GT图像(LR图像你可以按照原文生成):https://drive.google.com/file/d/1lSfv6SuMVO3FiNfEMh6uvmRDxjplze-q/view?usp=sharing
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不客气:)
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- 问题1、2:我只提供hr_train和hr_val的原因是:lr图像是在训练的时候随机一个scale下采样生成的 (这部分代码在https://github.com/iwuqing/ArSSR/blob/main/data.py 的30—38行),因此对于train_loader和val_loader,我们只需要提供高清patch即可。
- 问题3:测试集中的GT的size都是裁剪填充后的264×264×264。为了方便后续定量化计算:对于可整除的scale,直接下采样即可;对于不可整除的scale,需要对264×264×264的GT图像进行进一步的裁剪再下采样。比如对于scale=2,我们不需要进行裁剪;而对于scale=2.5这种,需要先裁剪到260×260×260,再进行下采样。
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是直接对3D图像进行下采样,用的是这个第三方库,其中order=3,其余参数默认 scipy.ndimage.interpolation.zoom。关于裁剪,直接进行数组裁剪即可,这部分可以参考https://github.com/iwuqing/ArSSR/blob/main/data.py 的30—38行
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我只下载了T1w的图像,别的模态并没下载。
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t1的所有图像共计1112张,共计20G,恐怕不太方便传输。太大了。我之前是用这个脚本进行下载的:https://github.com/huawei-lin/HCP_Dataset_Download_Automatically_Script
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因为在test的时候,输入的是图像patch。而在test的时候,输入的是完整图像,实际上占显存的部分在encoder部分。你可以用比较小的图像进行test,或者一个一个patch重建后再拼接。
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你把邮箱留一下,我回头找个时间上传网盘发你链接。因为分享原始数据集可能会涉及到license的问题。
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可以发给我一份吗,t1的所有图像共计1112张,共计20G 邮箱: [email protected] 我用您的脚本下载下来有300多个G,感觉不太对
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可以发给我一份吗,t1的所有图像共计1112张,共计20G 邮箱: [email protected] 我用您的脚本下载下来有300多个G,感觉不太对
已发你邮箱
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作者您好,我用您发的脚本下载的HCP_1200 t1w的图像数据,尺寸大小为(260,311,260),并不是您论文里尺寸为(320320256)您可以再提供一下HCP-1200 T1W的原始数据的网盘链接吗(之前的过期了)。第二个问题是,您是如何讲图片裁剪到264264264的?万分感谢您可以抽空回答我。邮箱:[email protected]
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作者您好,可以发给我一份您预处理后的数据吗,t1的所有图像共计1112张,共计20G 邮箱:[email protected]
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作者您好,可以发给我一份您预处理后的数据吗,t1的所有图像共计1112张,共计20G 邮箱:[email protected]
你好,你可以从以下百度网盘链接下载~
链接:https://pan.baidu.com/s/1ivy8TXUIO2nW35XK2ThafA
提取码:8m67
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Related Issues (15)
- 评价指标的代码 HOT 6
- quality metrics HOT 6
- 关于测试集下采样的问题 HOT 2
- some questions
- 你好,如何使用ANTs得到下面的这样的分割图? HOT 4
- 自动分割的问题 HOT 16
- Wrong command in README HOT 1
- training loss HOT 11
- HCP_1200 T1w原始数据 HOT 1
- HCP_1200 t1w 原始数据 HOT 1
- Question about evaluation metric calculation HOT 2
- Which HCP-1200 data set of skull dissection was used in the paper? HOT 1
- About inference time
- Unexpected key(s) in state_dict
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