1. E [tensorflow/stream_executor/cuda/cuda_driver.cc:466] failed call to cuInit: CUDA_ERROR_NO_DEVICE I [tensorflow/stream_executor/cuda/cuda_diagnostics.cc:86] kernel driver does not appear to be running on this host (ubuntu-G2): /proc/driver/nvidia/version does not exist 执行nvidia-smi显示： NVIDIA-SMI has failed because it couldn’t communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed and running. 解决办法：这是linux kernel 4.4.0-116-generic的一个小bug，降级到linux kernel 4.4.0-112-generic，再重装一下driver（不用重装cuda）问题就解决了，参考安装
1. Exception ignored in: <bound method BaseSession.__del__ of <tensorflow.python.client.session.Session object at 0x7fecee6109b0>>
Traceback (most recent call last): File “/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py”, line 686, in __del__ TypeError: ‘NoneType’ object is not callable 解决办法：这个问题不是大问题，就算不解决也可以使用，在代码中加入如下语句。
from keras import backend asK<br>K.clear_session()
2. TypeError: slice indices must be integers or None or have an __index__ method 解决办法： 这个问题一般都是数组在进行切片时，下标不是int型类型导致的，根据错误位置，修改下标为int型即可。
Task 1: Segmentation of gliomas in pre-operative MRI scans. The participants are called to address this task by using the provided clinically-acquired training data to develop their method and produce segmentation labels of the different glioma sub-regions. The sub-regions considered for evaluation are: 1) the “enhancing tumor” (ET), 2) the “tumor core” (TC), and 3) the “whole tumor” (WT) [see figure below]. The ET is described by areas that show hyper-intensity in T1Gd when compared to T1, but also when compared to “healthy” white matter in T1Gd. The TC describes the bulk of the tumor, which is what is typically resected. The TC entails the ET, as well as the necrotic (fluid-filled) and the non-enhancing (solid) parts of the tumor. The appearance of the necrotic (NCR) and the non-enhancing (NET) tumor core is typically hypo-intense in T1-Gd when compared to T1. The WT describes the complete extent of the disease, as it entails the TC and the peritumoral edema (ED), which is typically depicted by hyper-intense signal in FLAIR. The labels in the provided data are: 1 for NCR & NET, 2 for ED, 4 for ET, and 0 for everything else. The participants are called to upload their segmentation labels into CBICA’s Image Processing Portal for evaluation.
Task 2: Prediction of patient overall survival (OS) from pre-operative scans. Once the participants produce their segmentation labels in the pre-operative scans, they will be called to use these labels in combination with the provided multimodal MRI data to extract imaging/radiomic features that they consider appropriate, and analyze them through machine learning algorithms, in an attempt to predict patient OS. The participants do not need to be limited to volumetric parameters, but can also consider intensity, morphologic, histogram-based, and textural features, as well as spatial information, and glioma diffusion properties extracted from glioma growth models. Note that participants are expected to provide predicted survival status only for subjects with resection status of GTR (i.e., Gross Total Resection). The participants are called to upload a .csv file with the subject ids and the predicted survival values into CBICA’s Image Processing Portal for evaluation.