为什么这个图像对我来说是错误的渲染？

Question

我已经按照本笔记本教程中的代码进行操作，因此无法进行3d肺部分割

https://www.kaggle.com/gzuidhof/full-preprocessing-tutorial

除了以下更改之外，我都遵循了本教程：

1. 我已导入pydicom而不是dicom
2. 随后我将dicom.read_file（）更改为pydicom.dcmread（）

这是预期的图像，这就是我的肺部图像必须呈现的方式

这是我得到的渲染

plot_3d(segmented_lungs, 0)

但是，扫描的简单3d图看起来不错。

这是本教程的数据渲染：

这是我的数据渲染。所以一切都好，直到发生肺分割。

肺分割代码出了问题。

def largest_label_volume(im, bg=-1):
vals, counts = np.unique(im, return_counts=True)

counts = counts[vals != bg]
vals = vals[vals != bg]

if len(counts) > 0:
    return vals[np.argmax(counts)]
else:
    return None

def segment_lung_mask(image, fill_lung_structures=True): 

# not actually binary, but 1 and 2. 
# 0 is treated as background, which we do not want
binary_image = np.array(image > -320, dtype=np.int8)+1
labels = measure.label(binary_image)

# Pick the pixel in the very corner to determine which label is air.
#   Improvement: Pick multiple background labels from around the patient
#   More resistant to "trays" on which the patient lays cutting the air 
#   around the person in half
background_label = labels[0,0,0]

#Fill the air around the person
binary_image[background_label == labels] = 2


# Method of filling the lung structures (that is superior to something like 
# morphological closing)
if fill_lung_structures:
    # For every slice we determine the largest solid structure
    for i, axial_slice in enumerate(binary_image):
        axial_slice = axial_slice - 1
        labeling = measure.label(axial_slice)
        l_max = largest_label_volume(labeling, bg=0)

        if l_max is not None: #This slice contains some lung
            binary_image[i][labeling != l_max] = 1


binary_image -= 1 #Make the image actual binary
binary_image = 1-binary_image # Invert it, lungs are now 1

# Remove other air pockets insided body
labels = measure.label(binary_image, background=0)
l_max = largest_label_volume(labels, bg=0)
if l_max is not None: # There are air pockets
    binary_image[labels != l_max] = 0

return binary_image


segmented_lungs = segment_lung_mask(pix_resampled, False)
segmented_lungs_fill = segment_lung_mask(pix_resampled, True)


plot_3d(segmented_lungs, 0)