Interfaces

在之前已经学习过Interfaces的基本概念和操作，这里简单介绍一下。Interfaces就是其他软件包的python函数接口，想要使用其他软件的（例如FSL,SPM或者FreeSurfer）函数，就需要此函数的python接口和安装相应的软件包。

BET函数原始使用方法

先看一下我们需要去头骨的原始图像。

from nilearn.plotting import plot_anat
%matplotlib inline
plot_anat('/data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w.nii.gz', title='original',
      display_mode='ortho', dim=-1, draw_cross=False, annotate=False);

使用BET函数命令行形式（即在FSL中原始使用方法）

bet <input> <output>

在ipython中则需要在前面加%%bash

%%bash

FILENAME=/data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w

bet ${FILENAME}.nii.gz /output/sub-01_ses-test_T1w_bet.nii.gz

使用查看帮助信息

!bet -h

Usage:    bet <input> <output> [options]

Main bet2 options:
-o          generate brain surface outline overlaid onto original image
-m          generate binary brain mask
-s          generate approximate skull image
-n          don't generate segmented brain image output
-f <f>      fractional intensity threshold (0->1); default=0.5; smaller values give larger brain outline estimates
-g <g>      vertical gradient in fractional intensity threshold (-1->1); default=0; positive values give larger brain outline at bottom, smaller at top
-r <r>      head radius (mm not voxels); initial surface sphere is set to half of this
-c <x y z>  centre-of-gravity (voxels not mm) of initial mesh surface.
-t          apply thresholding to segmented brain image and mask
-e          generates brain surface as mesh in .vtk format

Variations on default bet2 functionality (mutually exclusive options):
(default)   just run bet2
-R          robust brain centre estimation (iterates BET several times)
-S          eye & optic nerve cleanup (can be useful in SIENA)
-B          bias field & neck cleanup (can be useful in SIENA)
-Z          improve BET if FOV is very small in Z (by temporarily padding end slices)
-F          apply to 4D FMRI data (uses -f 0.3 and dilates brain mask slightly)
-A          run bet2 and then betsurf to get additional skull and scalp surfaces (includes registrations)
-A2 <T2>    as with -A, when also feeding in non-brain-extracted T2 (includes registrations)

Miscellaneous options:
-v          verbose (switch on diagnostic messages)
-h          display this help, then exits
-d          debug (don't delete temporary intermediate images)

使用命令行生成二进制掩膜文件。

%%bash

FILENAME=/data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w

bet ${FILENAME}.nii.gz /output/sub-01_ses-test_T1w_bet.nii.gz -m

由此我们可知道python接口的函数实际上也是在命令行中输入需要的操作，不过使用的是python脚本形式。

BET函数接口使用

在之前有，这里简要介绍。

# import BET函数
from nipype.interfaces.fsl import BET
skullstrip = BET() # 对象实例化
skullstrip.inputs.in_file = "/data/ds000114/sub-01/ses-test/anat/ sub-01_ses-test_T1w.nii.gz" # 输入文件路径
skullstrip.inputs.out_file = "/output/T1w_nipype_bet.nii.gz" # 输出文件路径
res = skullstrip.run() # 运行

我们来看一下输出的命令行

print(skullstrip.cmdline)

bet /data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w.nii.gz /output/T1w_nipype_bet.nii.gz

与我们正常使用时的一样。

Interface errors

使用run方法运行接口时，对于FSL、Freesurfer和其他程序，会使系统输入上述命令行命令。对于MATLAB的程序如SPM，它会生成一个.m文件，然后在matlab环境下运行这个.m文件。

如果我们不给函数接口一个必要的输入参数它就会报错

skullstrip2 = BET()
try:
    skullstrip2.run()
except(ValueError) as err:
    print("ValueError:", err)
else:
    raise

ValueError: BET requires a value for input 'in_file'. For a list of required inputs, see BET.help()

BET函数没有输入文件的路径

又或者输入类型搞错，比如BET（）函数想要生成掩膜文件，你却把淹没文件的名字输入上去了 XD。

try:
skullstrip.inputs.mask = "mask_file.nii"
except(Exception) as err:
    if "TraitError" in str(err.__class__):
        print("TraitError:", err)
    else:
        raise
else:
    raise
TraitError: The 'mask' trait of a BETInputSpec instance must be a boolean, but a value of 'mask_file.nii' <class 'str'> was specified.

在报错的时候根据报错信息修改程序

参考

nipype_tutorial Interface