Comments (10)
是的,这样是可以的
from luna16-lung-nodule-analysis-2016-challenge.
rangz = lambda imagez,blockz: imagez if imagez < blockz else blockz
rangz = rangz(imagez,blockz)
貌似这样改就能得出数了~师兄对么?
from luna16-lung-nodule-analysis-2016-challenge.
打扰师兄了,但是这样也会出现新问题,在imagez < blockz的时候,rangz=imagez ,有个病例是imagez =14,这样的话在运行
hr_samples[0, 0:blockz, 0:block_width, 0:block_height] = image[0:rangz, 0:rangwidth, 0:rangheight]
时就会报错,报错内容:无法将(14,96,96)转换为(16,96,96),师兄是不是缺少这样情况的处理呀?您之前版本的data3dprepare.py在这方面貌似是做了判断的打扰您了谢谢~
from luna16-lung-nodule-analysis-2016-challenge.
您好,师兄,麻烦问您一下,vnet3d_predict.py测试程序的输入图像数据是data3dprepare.py处理后的数据嘛?如果是这样的话,那就相当于是:在已知结节质心位置进行分割了呀?但是测试新病例我们是不知道结节位置在哪里的呀~是不是我理解上有偏差?
from luna16-lung-nodule-analysis-2016-challenge.
imagez一般不会小于blockz的大小,如果碰到这种情况,可以补零处理
from luna16-lung-nodule-analysis-2016-challenge.
是的,首先要先确定结节的大致位置才可以进行分割的
from luna16-lung-nodule-analysis-2016-challenge.
是的,首先要先确定结节的大致位置才可以进行分割的
from luna16-lung-nodule-analysis-2016-challenge.
好的,收到多谢师兄
from luna16-lung-nodule-analysis-2016-challenge.
师兄你好,请问如果想测试自己的数据看看效果怎么样,但是却不知道结节的大致位置。这个有什么方法吗?
from luna16-lung-nodule-analysis-2016-challenge.
师兄你好,请问如果想测试自己的数据看看效果怎么样,但是却不知道结节的大致位置。这个有什么方法吗?
你好,可以拿luna16训练好的模型来检测你自己得数据,然后根据检测得区域得到区域中心坐标,再将坐标绘制出来。不过可能会出现很多假阳性结果。
from luna16-lung-nodule-analysis-2016-challenge.
Related Issues (20)
- How to test a complete 3D image? HOT 2
- Coordinate Transformation Problem HOT 3
- Augmain.py file is not executing HOT 2
- How to run inference on an external .mhd file? (which scripts and in which order) HOT 1
- 请问现在还提供数据集开放下载么
- 关于data3dprepare HOT 2
- 请问用作结节分割的mask的生成中是否是正负样本均有的 HOT 1
- Issues with subset.py
- GPU HOT 1
- 数据集问题 HOT 1
- dataprocess问题
- 如何为解决layer.py文件中“from ResNet3d.layer import (conv3d, normalizationlayer, max_pool3d, resnet_Add, weight_xavier_init, bias_variable, dense_to_one_hot)报错的问题” HOT 1
- 请问有没有pytorch版本的代码,感谢 HOT 1
- 良恶性分类 HOT 1
- 良恶性分类 HOT 3
- module 'tensorflow' has no attribute 'placeholder' HOT 1
- 文件缺失 HOT 1
- 数据集csv文件生成
- 良恶 trainloss不下降 HOT 1
- 结节假阳性二分类数据划分 HOT 1
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from luna16-lung-nodule-analysis-2016-challenge.