Question

我正在尝试使用概率值为整个幻灯片图像（WSI）生成热图或概率图。我有坐标点（确定WSI上的区域）和相应的概率值。

关于WSI的基本介绍：WSI尺寸很大（几乎100000 x 100000像素）。因此，无法使用常规图像查看器打开这些图像。使用 OpenSlide软件处理WSI。

我以前在Stack Overflow上看到过与热图有关的文章，但是由于以不同的方式处理WSI，所以我无法弄清楚如何应用这些解决方案。我遵循的一些示例：1，2，3，4，5，6，7，{{ 3}}，8，9，10等。

Answer 1

要在WSI上生成热图，请遵循以下说明：

首先提取图像补丁并保存坐标。使用以下代码提取补丁。根据要求，代码需要进行一些更改。该代码已从以下位置复制：patch extraction code link

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse
import logging
try:
    import Image
except:
    from PIL import Image
import math
import numpy as np
import openslide
import os
from time import strftime,gmtime

parser = argparse.ArgumentParser(description='Extract a series of patches from a whole slide image')
parser.add_argument("-i", "--image", dest='wsi',  nargs='+', required=True, help="path to a whole slide image")
parser.add_argument("-p", "--patch_size", dest='patch_size', default=299, type=int, help="pixel width and height for patches")
parser.add_argument("-b", "--grey_limit", dest='grey_limit', default=0.8, type=float, help="greyscale value to determine if there is sufficient tissue present [default: `0.8`]")
parser.add_argument("-o", "--output", dest='output_name', default="output", help="Name of the output file directory [default: `output/`]")

parser.add_argument("-v", "--verbose",
            dest="logLevel",
            choices=['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'],
            default="INFO",
            help="Set the logging level")
args = parser.parse_args()

if args.logLevel:
        logging.basicConfig(level=getattr(logging, args.logLevel))

wsi=' '.join(args.wsi)


""" Set global variables """
mean_grey_values = args.grey_limit * 255
number_of_useful_regions = 0
wsi=os.path.abspath(wsi)
outname=os.path.abspath(args.output_name)
basename = os.path.basename(wsi)
level = 0

def main():
    img,num_x_patches,num_y_patches = open_slide()
    logging.debug('img: {}, num_x_patches = {}, num_y_patches: {}'.format(img,num_x_patches,num_y_patches))

    for x in range(num_x_patches):
        for y in range(num_y_patches):
            img_data = img.read_region((x*args.patch_size,y*args.patch_size),level, (args.patch_size, args.patch_size))
            print_pics(x*args.patch_size,y*args.patch_size,img_data,img)

    pc_uninformative = number_of_useful_regions/(num_x_patches*num_y_patches)*100
    pc_uninformative = round(pc_uninformative,2)
    logging.info('Completed patch extraction of {} images.'.format(number_of_useful_regions))
    logging.info('{}% of the image is uninformative\n'.format(pc_uninformative))


def print_pics(x_top_left,y_top_left,img_data,img):
    if x_top_left % 100 == 0 and y_top_left % 100 == 0 and x_top_left != 0:
        pc_complete = round(x_top_left /img.level_dimensions[0][0],2) * 100
        logging.info('{:.2f}% Complete at {}'.format(pc_complete,strftime("%a, %d %b %Y %H:%M:%S +0000", gmtime())))
        exit()

    img_data_np = np.array(img_data)
    """ Convert to grayscale"""
    grey_img = rgb2gray(img_data_np)
    if np.mean(grey_img) < mean_grey_values:
        logging.debug('Image grayscale = {} compared to threshold {}'.format(np.mean(grey_img),mean_grey_values))
        global number_of_useful_regions
        number_of_useful_regions += 1
                wsi_base = os.path.basename(wsi)
                wsi_base = wsi_base.split('.')[0]
        img_name = wsi_base + "_" + str(x_top_left) + "_" + str(y_top_left) + "_" + str(args.patch_size)
        #write_img_rotations(img_data_np,img_name)
                logging.debug('Saving {} {} {}'.format(x_top_left,y_top_left,np.mean(grey_img)))
        save_image(img_data_np,1,img_name)

def gen_x_and_y(xlist,ylist,img):
    for x in xlist:
        for y in ylist:
            img_data = img.read_region((x*args.patch_size,y*args.patch_size),level, (args.patch_size, args.patch_size))
            yield (x, y,img_data)

def open_slide():
    """
    The first level is always the main image
    Get width and height tuple for the first level
    """

    logging.debug('img: {}'.format(wsi))

    img = openslide.OpenSlide(wsi)
    img_dim = img.level_dimensions[0]

    """
    Determine what the patch size should be, and how many iterations it will take to get through the WSI
    """
    num_x_patches = int(math.floor(img_dim[0] / args.patch_size))
    num_y_patches = int(math.floor(img_dim[1] / args.patch_size))

    remainder_x = img_dim[0] % num_x_patches
    remainder_y = img_dim[1] % num_y_patches

    logging.debug('The WSI shape is {}'.format(img_dim))
    logging.debug('There are {} x-patches and {} y-patches to iterate through'.format(num_x_patches,num_y_patches))
    return img,num_x_patches,num_y_patches

def validate_dir_exists():
    if os.path.isdir(outname) == False:
        os.mkdir(outname)
    logging.debug('Validated {} directory exists'.format(outname))
    if os.path.exists(wsi):
        logging.debug('Found the file {}'.format(wsi))
    else:
        logging.debug('Could not find the file {}'.format(wsi))
        exit()

def rgb2gray(rgb):
    """Converts an RGB image into grayscale """
    r, g, b = rgb[:,:,0], rgb[:,:,1], rgb[:,:,2]
    gray = 0.2989 * r + 0.5870 * g + 0.1140 * b
    return gray

def save_image(img,j,img_name):
        tmp = os.path.join(outname,img_name+"_"+str(j)+".png")
    try:
        im = Image.fromarray(img)
        im.save(tmp)
    except:
        print('Could not print {}'.format(tmp))
                exit()
if __name__ == '__main__':
    validate_dir_exists()
    main()

第二，生成每个补丁的概率值。 最后，将坐标内的所有像素值替换为相应的概率值，并使用色图显示结果。

这是在WSI上生成热图的基本思想。您可以根据需要修改代码和概念以获得热点图。

Answer 2

我们开发了一个用于处理整个幻灯片图像的 python 包： https://github.com/amirakbarnejad/PyDmed 这是获取整个幻灯片图像的热图的教程： https://amirakbarnejad.github.io/Tutorial/tutorial_section5.html。这里还有一个示例笔记本，它使用 PyDmed 获取 WSI 的热图： Link to the sample notebook.

PyDmed 的好处是它是多处理的。数据加载器向 GPU 发送补丁流，而 StreamWriter 在单独的进程中写入磁盘。因此，它的效率很高。运行时间当然取决于机器、WSI 的大小等。在具有良好 GPU 的好机器上，PyDmed 可以在一天内生成大约 120 个 WSI 的热图。

如何使用某些概率值在整个幻灯片图像（.svs格式）上生成热图？

2 个答案: