{"id":45680,"date":"2025-07-30T00:51:07","date_gmt":"2025-07-29T16:51:07","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/45680.html"},"modified":"2025-07-30T00:51:07","modified_gmt":"2025-07-29T16:51:07","slug":"%e5%9c%a8%e6%9c%8d%e5%8a%a1%e5%99%a8%e4%b8%8a%e7%94%a8%e8%87%aa%e5%b7%b1%e7%9a%84%e4%ba%8c%e7%bb%b4-%e4%b8%89%e7%bb%b4%e6%95%b0%e6%8d%ae%e9%9b%86-png-nrrd%e7%ad%89%e8%b7%91nnunetv2","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/45680.html","title":{"rendered":"\u5728\u670d\u52a1\u5668\u4e0a\u7528\u81ea\u5df1\u7684\u4e8c\u7ef4\/\u4e09\u7ef4\u6570\u636e\u96c6(.png\/.nrrd\u7b49)\u8dd1nnUNetv2"},"content":{"rendered":"<h3 id=\"%E5%89%8D%E8%A8%80\">\u524d\u8a00<\/h3>\n<p id=\"%E5%9C%A8%E9%85%8D%E7%BD%AE%E7%8E%AF%E5%A2%83%E8%BF%87%E7%A8%8B%E4%B8%AD%E5%87%BA%E7%8E%B0%E7%9A%84%E9%97%AE%E9%A2%98%E5%8F%AF%E5%8F%82%E8%80%83%E8%BF%99%E7%AF%87%E6%96%87%E7%AB%A0\">\u8fd9\u7bc7\u662f\u6211\u81ea\u5df1\u7684\u64cd\u4f5c\u7b14\u8bb0&#xff0c;\u7f51\u4e0a\u53ef\u4ee5\u641c\u5230\u597d\u591a\u76f8\u4f3c\u6587\u7ae0&#xff0c;\u5927\u5bb6\u7efc\u5408\u6765\u770b&#xff0c;\u5bfb\u627e\u9002\u5408\u81ea\u5df1\u7684&#xff0c;\u5728\u914d\u7f6e\u73af\u5883\u8fc7\u7a0b\u4e2d\u51fa\u73b0\u7684\u95ee\u9898\u53ef\u53c2\u8003\u8fd9\u7bc7\u6587\u7ae0\u3010\u65b0\u624b\u5c0f\u767d\u3011\u5728Linux\u670d\u52a1\u5668\u6216\u672c\u5730IDE&#xff0c;\u8dd1\u6df1\u5ea6\u5b66\u4e60\u4ee3\u7801\u6307\u5357<\/p>\n<p id=\"main-toc\">\u76ee\u5f55<\/p>\n<p id=\"%E5%89%8D%E8%A8%80-toc\" style=\"margin-left:40px\">\u524d\u8a00<\/p>\n<p id=\"1.%20%E4%B8%8B%E8%BD%BD%E4%BB%A3%E7%A0%81%E5%B9%B6%E9%85%8D%E7%BD%AE%E7%8E%AF%E5%A2%83-toc\" style=\"margin-left:40px\">1. \u4e0b\u8f7d\u4ee3\u7801\u5e76\u914d\u7f6e\u73af\u5883<\/p>\n<p id=\"2.%20%E5%87%86%E5%A4%87%E6%95%B0%E6%8D%AE%E9%9B%86-toc\" style=\"margin-left:40px\">2. \u51c6\u5907\u6570\u636e\u96c6<\/p>\n<p id=\"%E4%BA%8C%E7%BB%B4%E6%95%B0%E6%8D%AE%E9%9B%86%EF%BC%88%E5%A6%82.png%EF%BC%89-toc\" style=\"margin-left:80px\">\u4e8c\u7ef4\u6570\u636e\u96c6&#xff08;\u5982.png&#xff09;<\/p>\n<p id=\"%E4%B8%89%E7%BB%B4%E6%95%B0%E6%8D%AE%E9%9B%86%EF%BC%88%E5%A6%82.nrrd%EF%BC%89-toc\" style=\"margin-left:80px\">\u4e09\u7ef4\u6570\u636e\u96c6&#xff08;\u5982.nrrd&#xff09;<\/p>\n<p id=\"3.%20%E5%BC%80%E5%A7%8B%E8%AE%AD%E7%BB%83-toc\" style=\"margin-left:40px\">3. \u5f00\u59cb\u8bad\u7ec3<\/p>\n<p id=\"4.%20%E9%AA%8C%E8%AF%81%E5%92%8C%E6%8E%A8%E7%90%86-toc\" style=\"margin-left:40px\">4. \u9a8c\u8bc1\u548c\u63a8\u7406<\/p>\n<p id=\"4.%20%E5%85%B6%E4%BB%96-toc\" style=\"margin-left:40px\">5. \u5176\u4ed6<\/p>\n<hr \/>\n<h3 id=\"1.%20%E4%B8%8B%E8%BD%BD%E4%BB%A3%E7%A0%81%E5%B9%B6%E9%85%8D%E7%BD%AE%E7%8E%AF%E5%A2%83\">1. \u4e0b\u8f7d\u4ee3\u7801\u5e76\u914d\u7f6e\u73af\u5883<\/h3>\n<p>\u6253\u5f00vscode\u6216pycharm\u7b49\u7f16\u8f91\u5668&#xff0c;\u8fdb\u5165\u5230\u4f60\u60f3\u5b58\u653e\u4ee3\u7801\u7684\u6587\u4ef6\u5939\u4e0b\u3002<\/p>\n<p>\u9009\u62e91&#xff1a;\u5728\u7ec8\u7aef\u8f93\u5165\u547d\u4ee4&#xff0c;\u76f4\u63a5\u4e0b\u8f7d\u4ee3\u7801<\/p>\n<p>git clone https:\/\/github.com\/MIC-DKFZ\/nnUNet.git <\/p>\n<p>\u9009\u62e92&#xff1a;\u5728github\u4e0b\u8f7d\u4ee3\u7801\u538b\u7f29\u5305&#xff0c;\u518d\u624b\u52a8\u4e0a\u4f20\u5230\u670d\u52a1\u5668\u3002<\/p>\n<p>\u6b64\u65f6\u76ee\u5f55\u957f\u8fd9\u6837<\/p>\n<p class=\"img-center\"><img loading=\"lazy\" decoding=\"async\" alt=\"\" height=\"171\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2025\/07\/20250729165106-6888fbfa48a74.png\" width=\"114\" \/><\/p>\n<p>\u00a0\u5148\u521b\u5efa\u4e00\u4e2a\u65b0\u7684conda\u73af\u5883\u5e76\u542f\u52a8\u73af\u5883<\/p>\n<p>conda create -n nnunet python&#061;3.10<br \/>\nconda activate nnunet <\/p>\n<p>\u518d\u7ed9\u73af\u5883\u5b89\u88c5pytorch\u5305&#xff0c;\u5148\u8f93\u5165\u5982\u4e0b\u547d\u4ee4&#xff0c;\u67e5\u770bCUDA Version\u662f\u591a\u5c11<\/p>\n<p>nvidia-smi <\/p>\n<p>\u6211\u7684\u662fCUDA Version: 12.4\u00a0&#xff0c;\u6545\u9009\u62e9\u4f4e\u4e8eCUDA12.4\u7684pytorch&#xff0c;\u5982CUDA11.8&#xff0c;\u7ec8\u7aef\u8f93\u5165\u5982\u4e0b\u547d\u4ee4<\/p>\n<p>pip3 install torch torchvision torchaudio &#8211;index-url https:\/\/download.pytorch.org\/whl\/cu118 <\/p>\n<p>\u5728\u7ec8\u7aef&#xff0c;\u8fdb\u5165nnUNet\u76ee\u5f55\u540e&#xff0c;\u9010\u6b65\u8f93\u5165\u4ee5\u4e0b\u547d\u4ee4&#xff08;\u4e5f\u53ef\u4ee5\u624b\u52a8\u4e0b\u8f7d\u540e\u4e0a\u4f20\u670d\u52a1\u5668&#xff1a;hiddenlayer&#xff09;<\/p>\n<p>pip install -i https:\/\/pypi.tuna.tsinghua.edu.cn\/simple -e .<br \/>\ngit clone https:\/\/github.com\/FabianIsensee\/hiddenlayer.git<br \/>\ncd hiddenlayer<br \/>\npip install -i https:\/\/pypi.tuna.tsinghua.edu.cn\/simple -e . <\/p>\n<hr \/>\n<h3 id=\"2.%20%E5%87%86%E5%A4%87%E6%95%B0%E6%8D%AE%E9%9B%86\">2. \u51c6\u5907\u6570\u636e\u96c6<\/h3>\n<p>\u6b64\u5904\u5206\u4e3a\u4e24\u79cd\u60c5\u51b5<\/p>\n<ul>\n<li>\u4e8c\u7ef4\u6570\u636e\u96c6&#xff08;\u5982 .png\u3001jpg \u7b49&#xff09;<\/li>\n<li>\u4e09\u7ef4\u6570\u636e\u96c6&#xff08;\u5982 .nrrd\u3001niii.gz \u7b49&#xff09;<\/li>\n<\/ul>\n<h4 id=\"%E4%BA%8C%E7%BB%B4%E6%95%B0%E6%8D%AE%E9%9B%86%EF%BC%88%E5%A6%82.png%EF%BC%89\">\u4e8c\u7ef4\u6570\u636e\u96c6&#xff08;\u5982.png&#xff09;<\/h4>\n<p>\u5728nnUNet\u76ee\u5f55\u4e0b&#xff0c;\u65b0\u5efa\u6587\u4ef6\u5939nnUNetFrame&#xff0c;DATASET&#xff0c;nnUNet_raw&#xff1b;nnUNet_preprocessed&#xff1b;nnUNet_results&#xff0c;nnUnet_raw_data&#xff0c;\u4ee5\u53catest&#xff1b;train&#xff1b;valid&#xff0c;\u5728\u8fd9\u4e09\u4e2a\u76ee\u5f55\u4e0b\u5206\u522b\u521b\u5efa images&#xff1b;labels<\/p>\n<p>\u6b64\u65f6\u76ee\u5f55\u957f\u8fd9\u6837<\/p>\n<p class=\"img-center\"><img loading=\"lazy\" decoding=\"async\" alt=\"\" height=\"442\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2025\/07\/20250729165106-6888fbfa52dd7.png\" width=\"225\" \/><\/p>\n<p>\u63a5\u4e0b\u6765\u4e0a\u4f20 \u8bad\u7ec3\u3001\u6d4b\u8bd5\u3001\u9a8c\u8bc1\u6570\u636e\u96c6\u5230 images \u548c labels&#xff0c;\u6211\u7528\u7684\u90fd\u662f.png\u683c\u5f0f\u7684\u56fe\u7247&#xff0c;images\u91cc\u653e\u539f\u59cb\u56fe\u50cf&#xff08;shape&#xff1a;512, 512, 3&#xff09;&#xff0c;labels\u653emask\u63a9\u7801\u56fe\u50cf&#xff08;shape: 512, 512&#xff09;<\/p>\n<p>\u7136\u540e&#xff0c;\u7ec8\u7aef\u8f93\u5165\u5982\u4e0b\u5185\u5bb9&#xff0c;\u90fd\u662f\u54b1\u521a\u521a\u521b\u5efa\u597d\u7684\u76ee\u5f55&#xff08;\u8fd9\u4e00\u6b65\u9aa4&#xff0c;\u6bcf\u91cd\u65b0\u6253\u5f00\u4e00\u6b21\u7535\u8111\u90fd\u8981\u518d\u8fd0\u884c\u4e00\u904d&#xff09;<\/p>\n<p>export nnUNet_raw&#061;&#034;\u6362\u6210\u4f60\u81ea\u5df1\u7684\u8def\u5f84\/nnUNet\/nnUNetFrame\/DATASET\/nnUNet_raw&#034;<br \/>\nexport nnUNet_preprocessed&#061;&#034;\u6362\u6210\u4f60\u81ea\u5df1\u7684\u8def\u5f84\/nnUNet\/nnUNetFrame\/DATASET\/nnUNet_preprocessed&#034;<br \/>\nexport nnUNet_results&#061;&#034;\u6362\u6210\u4f60\u81ea\u5df1\u7684\u8def\u5f84\/nnUNet\/nnUNetFrame\/DATASET\/nnUNet_results&#034; <\/p>\n<p>\u63a5\u4e0b\u6765&#xff0c;\u5230\u8fd9\u4e2a\u8def\u5f84\u4e0b&#xff1a;nnUNet\/nnunetv2\/dataset_conversion\u627e\u5230Dataset120_RoadSegmentation.py<\/p>\n<p>\u53ef\u4ee5\u628a\u5982\u4e0b\u5185\u5bb9\u5168\u90e8\u590d\u5236\u66ff\u6362\u6389\u539f\u5185\u5bb9&#xff0c;\u518d\u6309\u7167\u4ee3\u7801\u91cc\u9762\u7684\u6ce8\u91ca&#xff0c;\u6539\u6210\u81ea\u5df1\u7684\u5185\u5bb9&#xff0c;\u5e76\u8fd0\u884c<\/p>\n<p>import multiprocessing<br \/>\nimport shutil<br \/>\nfrom multiprocessing import Pool<\/p>\n<p>from batchgenerators.utilities.file_and_folder_operations import *<\/p>\n<p>from nnunetv2.dataset_conversion.generate_dataset_json import generate_dataset_json<br \/>\nfrom nnunetv2.paths import nnUNet_raw<br \/>\nfrom skimage import io<br \/>\nfrom acvl_utils.morphology.morphology_helper import generic_filter_components<br \/>\nfrom scipy.ndimage import binary_fill_holes<\/p>\n<p>def load_and_convert_case(input_image: str, input_seg: str, output_image: str, output_seg: str,<br \/>\n                          min_component_size: int &#061; 50):<br \/>\n    seg &#061; io.imread(input_seg)<br \/>\n    seg[seg &#061;&#061; 255] &#061; 1<\/p>\n<p>    image &#061; io.imread(input_image)<br \/>\n    if image.ndim &#061;&#061; 3:  # \u5982\u679c\u662fRGB\u56fe\u50cf<br \/>\n        image &#061; image.sum(2)  # \u5408\u5e76\u901a\u9053<br \/>\n    elif image.ndim &#061;&#061; 2:  # \u5982\u679c\u662f\u7070\u5ea6\u56fe\u50cf<br \/>\n        pass  # \u4fdd\u6301\u539f\u6837<br \/>\n    else:<br \/>\n        raise ValueError(f&#034;\u4e0d\u652f\u6301\u7684\u56fe\u50cf\u7ef4\u5ea6: {image.shape}&#034;)<\/p>\n<p>    mask &#061; image &#061;&#061; (3 * 255) if image.ndim &#061;&#061; 2 else image &#061;&#061; 255<\/p>\n<p>    mask &#061; generic_filter_components(mask, filter_fn&#061;lambda ids, sizes: [i for j, i in enumerate(ids) if<br \/>\n                                                                         sizes[j] &gt; min_component_size])<br \/>\n    mask &#061; binary_fill_holes(mask)<\/p>\n<p>    seg[mask] &#061; 0<\/p>\n<p>    io.imsave(output_seg, seg, check_contrast&#061;False)<br \/>\n    shutil.copy(input_image, output_image)<\/p>\n<p>if __name__ &#061;&#061; &#034;__main__&#034;:<br \/>\n    source &#061; &#039;\u8fd9\u91cc\u6362\u6210\u4f60\u81ea\u5df1\u7684\u8def\u5f84\u54e6&#xff0c;\u9009\u62e9\u5230\u653etrain&#xff0c;test,valid\u7684\u6587\u4ef6\u5939\/nnUNet\/nnUNetFrame\/DATASET\/nnUNet_raw\/nnUnet_raw_data&#039;<br \/>\n    dataset_name &#061; &#039;\u8fd9\u91cc\u9700\u8981\u4f60\u53d6\u4e00\u4e2a\u540d\u5b57&#xff0c;\u5fc5\u987b\u5f62\u5982&#xff1a;Dataset\u6570\u636e\u96c6\u53f73\u4f4d\u6570\u5b57_\u6570\u636e\u96c6\u540d\u79f0&#xff0c;\u5982Dataset999_DATA&#039;<\/p>\n<p>    imagestr &#061; join(nnUNet_raw, dataset_name, &#039;imagesTr&#039;)<br \/>\n    imagests &#061; join(nnUNet_raw, dataset_name, &#039;imagesTs&#039;)<br \/>\n    labelstr &#061; join(nnUNet_raw, dataset_name, &#039;labelsTr&#039;)<br \/>\n    labelsts &#061; join(nnUNet_raw, dataset_name, &#039;labelsTs&#039;)<br \/>\n    maybe_mkdir_p(imagestr)<br \/>\n    maybe_mkdir_p(imagests)<br \/>\n    maybe_mkdir_p(labelstr)<br \/>\n    maybe_mkdir_p(labelsts)<\/p>\n<p>    train_source &#061; join(source, &#039;train&#039;)<br \/>\n    test_source &#061; join(source, &#039;test&#039;)<\/p>\n<p>    with multiprocessing.get_context(&#034;spawn&#034;).Pool(8) as p:<\/p>\n<p>        valid_ids &#061; subfiles(join(train_source, &#039;images&#039;), join&#061;False, suffix&#061;&#039;png&#039;)<br \/>\n        num_train &#061; 0<br \/>\n        r &#061; []<br \/>\n        for v in valid_ids:<br \/>\n            label_path &#061; join(train_source, &#039;labels&#039;, v)<br \/>\n            if isfile(label_path):  # \u786e\u8ba4\u6807\u7b7e\u6587\u4ef6\u5b58\u5728<br \/>\n                num_train &#043;&#061; 1<br \/>\n                r.append(<br \/>\n                    p.starmap_async(<br \/>\n                        load_and_convert_case,<br \/>\n                        ((<br \/>\n                             join(train_source, &#039;images&#039;, v),<br \/>\n                             label_path,<br \/>\n                             join(imagestr, v[:-4] &#043; &#039;_0000.png&#039;),<br \/>\n                             join(labelstr, v),<br \/>\n                             50<br \/>\n                         ),)<br \/>\n                    )<br \/>\n                )<br \/>\n            else:<br \/>\n                print(f&#034;\u8b66\u544a: \u56fe\u50cf {v} \u6ca1\u6709\u5bf9\u5e94\u7684\u6807\u7b7e\u6587\u4ef6&#xff0c;\u5df2\u8df3\u8fc7&#034;)<\/p>\n<p>        valid_ids &#061; subfiles(join(test_source, &#039;labels&#039;), join&#061;False, suffix&#061;&#039;png&#039;)<br \/>\n        for v in valid_ids:<br \/>\n            image_path &#061; join(test_source, &#039;images&#039;, v)<br \/>\n            label_path &#061; join(test_source, &#039;labels&#039;, v)<br \/>\n            if isfile(image_path) and isfile(label_path):  # \u786e\u8ba4\u56fe\u50cf\u548c\u6807\u7b7e\u6587\u4ef6\u90fd\u5b58\u5728<br \/>\n                r.append(<br \/>\n                    p.starmap_async(<br \/>\n                        load_and_convert_case,<br \/>\n                        ((<br \/>\n                             image_path,<br \/>\n                             label_path,<br \/>\n                             join(imagests, v[:-4] &#043; &#039;_0000.png&#039;),<br \/>\n                             join(labelsts, v),<br \/>\n                             50<br \/>\n                         ),)<br \/>\n                    )<br \/>\n                )<br \/>\n            else:<br \/>\n                print(f&#034;\u8b66\u544a: \u6d4b\u8bd5\u56fe\u50cf {v} \u6216\u5176\u6807\u7b7e\u6587\u4ef6\u4e0d\u5b58\u5728&#xff0c;\u5df2\u8df3\u8fc7&#034;)<\/p>\n<p>        _ &#061; [i.get() for i in r]<\/p>\n<p>    generate_dataset_json(join(nnUNet_raw, dataset_name), {0: &#039;R&#039;, 1: &#039;G&#039;, 2: &#039;B&#039;}, {&#039;background&#039;: 0, &#039;\u8fd9\u91cc\u662f\u4f60\u8981\u5206\u5272\u7684\u7c7b\u522b\u540d\u79f0&#xff0c;\u5982cat&#039;: 1},<br \/>\n                          num_train, &#039;.png&#039;, dataset_name&#061;dataset_name) <\/p>\n<p>\u6b64\u65f6&#xff0c;nnUNet_raw\u6587\u4ef6\u5939\u4e2d\u4f1a\u51fa\u73b0\u5982\u4e0b\u5185\u5bb9<\/p>\n<p>images\u4e2d\u7684\u547d\u540d\u5f62\u59821_0000_0000.png\u30011_0000_0001.png\u30011_0000_0002.png&#xff0c;\u4e00\u5f20\u56fe\u5206\u6210\u4e86\u4e09\u5f20&#xff0c;\u4ee3\u8868\u4e09\u4e2a\u901a\u9053<\/p>\n<p>labels\u4e2d\u7684\u547d\u540d\u5f62\u59821_0000.png\u30011_0001.png\u30011_0002.png&#xff0c;\u70b9\u5f00labels\u4e2d\u7684\u56fe\u7247\u770b&#xff0c;\u662f\u5168\u9ed1\u7684<\/p>\n<p class=\"img-center\"><img loading=\"lazy\" decoding=\"async\" alt=\"\" height=\"195\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2025\/07\/20250729165106-6888fbfa5fda7.png\" width=\"170\" \/><\/p>\n<p>ps&#xff1a;labelsTs\u8fd9\u4e2a\u76ee\u5f55\u4e0d\u662f\u5fc5\u987b\u8981\u6709\u7684&#xff0c;\u662f\u6211\u8fd9\u4e2a\u6570\u636e\u96c6\u91cc\u6b63\u597d\u6709&#xff0c;\u5c31\u4e00\u8d77\u653e\u4e0a\u6765\u4e86<\/p>\n<p>dataset.json\u4e2d\u7684\u5185\u5bb9\u5982\u4e0b&#xff0c;\u8fd9\u662f\u6211\u751f\u6210\u51fa\u6765\u7684&#xff0c;\u4ee5\u4f9b\u53c2\u8003&#xff1a;<\/p>\n<p>{<br \/>\n    &#034;channel_names&#034;: {<br \/>\n        &#034;0&#034;: &#034;R&#034;,<br \/>\n        &#034;1&#034;: &#034;G&#034;,<br \/>\n        &#034;2&#034;: &#034;B&#034;<br \/>\n    },<br \/>\n    &#034;labels&#034;: {<br \/>\n        &#034;background&#034;: 0,<br \/>\n        &#034;stenosis&#034;: 1<br \/>\n    },<br \/>\n    &#034;numTraining&#034;: 997,<br \/>\n    &#034;file_ending&#034;: &#034;.png&#034;,<br \/>\n    &#034;licence&#034;: &#034;Whoever converted this dataset was lazy and didn&#039;t look it up!&#034;,<br \/>\n    &#034;converted_by&#034;: &#034;Please enter your name, especially when sharing datasets with others in a common infrastructure!&#034;,<br \/>\n    &#034;name&#034;: &#034;Dataset001_ARCADE&#034;<br \/>\n} <\/p>\n<p>\u63a5\u4e0b\u6765&#xff0c;\u7ec8\u7aef\u8f93\u5165\u5982\u4e0b\u5185\u5bb9<\/p>\n<p>nnUNetv2_plan_and_preprocess -d \u6570\u636e\u96c6\u53f73\u4f4d &#8211;verify_dataset_integrity <\/p>\n<p>\u6b64\u65f6&#xff0c;nnUNet_preprocessed\u76ee\u5f55\u51fa\u73b0\u5982\u4e0b\u5185\u5bb9<\/p>\n<p class=\"img-center\"><img loading=\"lazy\" decoding=\"async\" alt=\"\" height=\"174\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2025\/07\/20250729165106-6888fbfa685fa.png\" width=\"184\" \/><\/p>\n<hr \/>\n<h4 id=\"%E4%B8%89%E7%BB%B4%E6%95%B0%E6%8D%AE%E9%9B%86%EF%BC%88%E5%A6%82.nrrd%EF%BC%89\">\u4e09\u7ef4\u6570\u636e\u96c6&#xff08;\u5982.nrrd&#xff09;<\/h4>\n<li>\u5728nnUNet\u76ee\u5f55\u4e0b&#xff0c;\u65b0\u5efa\u6587\u4ef6\u5939nnUNetFrame&#xff0c;DATASET&#xff0c;nnUNet_raw&#xff1b;nnUNet_preprocessed&#xff1b;nnUNet_results<\/li>\n<li>\u7136\u540e\u521b\u5efa\u6587\u4ef6\u5939 \u201cDatasetXXX&#xff08;\u6570\u636e\u96c6\u53f7&#xff1a;\u4efb\u610f3\u4f4d\u6570\u5b57&#xff09;_XXX&#xff08;\u6570\u636e\u96c6\u540d\u79f0&#xff09;\u201d&#xff0c;\u5728\u8fd9\u4e2a\u76ee\u5f55\u4e0b\u5206\u522b\u521b\u5efa imagesTr&#xff1b;imagesTs&#xff1b;labelsTr&#xff0c;\u5206\u522b\u653e\u5165\u4f60\u8981\u8bad\u7ec3\u7684\u6570\u636e&#xff0c;\u5982\u201c1_0000.nrrd\u201d&#xff0c;\u6570\u636e\u547d\u540d \u5b98\u65b9\u6587\u6863<\/li>\n<li>\u518d\u65b0\u5efa\u4e00\u4e2adataset.json\u6587\u4ef6&#xff0c;json\u6587\u4ef6\u5185\u5bb9 \u5b98\u65b9\u6587\u6863<\/li>\n<p>\u6b64\u65f6\u76ee\u5f55\u6548\u679c\u5982\u4e0b&#xff1a;<\/p>\n<p class=\"img-center\"><img loading=\"lazy\" decoding=\"async\" alt=\"\" height=\"350\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2025\/07\/20250729165106-6888fbfa703c6.png\" width=\"180\" \/><\/p>\n<p>dataset.json\u4e2d\u7684\u5185\u5bb9\u5982\u4e0b&#xff0c;\u8fd9\u662f\u6211\u751f\u6210\u51fa\u6765\u7684&#xff0c;\u4ee5\u4f9b\u53c2\u8003&#xff1a;<\/p>\n<p>{<br \/>\n    &#034;channel_names&#034;: {<br \/>\n        &#034;0&#034;: &#034;Artery&#034;<br \/>\n    },<br \/>\n    &#034;labels&#034;: {<br \/>\n        &#034;background&#034;: 0,<br \/>\n        &#034;artery&#034;: 1<br \/>\n    },<br \/>\n    &#034;numTraining&#034;: 40,<br \/>\n    &#034;file_ending&#034;: &#034;.nrrd&#034;<br \/>\n} <\/p>\n<p>\u7136\u540e&#xff0c;\u7ec8\u7aef\u8f93\u5165\u5982\u4e0b\u5185\u5bb9&#xff0c;\u90fd\u662f\u54b1\u521a\u521a\u521b\u5efa\u597d\u7684\u76ee\u5f55&#xff08;\u8fd9\u4e00\u6b65\u9aa4&#xff0c;\u6bcf\u91cd\u65b0\u6253\u5f00\u4e00\u6b21\u7535\u8111\u90fd\u8981\u518d\u8fd0\u884c\u4e00\u904d&#xff09;<\/p>\n<p>export nnUNet_raw&#061;&#034;\u6362\u6210\u4f60\u81ea\u5df1\u7684\u8def\u5f84\/nnUNet\/nnUNetFrame\/DATASET\/nnUNet_raw&#034;<br \/>\nexport nnUNet_preprocessed&#061;&#034;\u6362\u6210\u4f60\u81ea\u5df1\u7684\u8def\u5f84\/nnUNet\/nnUNetFrame\/DATASET\/nnUNet_preprocessed&#034;<br \/>\nexport nnUNet_results&#061;&#034;\u6362\u6210\u4f60\u81ea\u5df1\u7684\u8def\u5f84\/nnUNet\/nnUNetFrame\/DATASET\/nnUNet_results&#034; <\/p>\n<p>\u00a0\u63a5\u4e0b\u6765&#xff0c;\u7ec8\u7aef\u8f93\u5165\u5982\u4e0b\u5185\u5bb9<\/p>\n<p>nnUNetv2_plan_and_preprocess -d \u6570\u636e\u96c6\u53f73\u4f4d &#8211;verify_dataset_integrity <\/p>\n<p>\u6b64\u65f6&#xff0c;nnUNet_preprocessed\u76ee\u5f55\u51fa\u73b0\u5982\u4e0b\u5185\u5bb9<\/p>\n<p class=\"img-center\"><img loading=\"lazy\" decoding=\"async\" alt=\"\" height=\"221\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2025\/07\/20250729165106-6888fbfa7badc.png\" width=\"183\" \/><\/p>\n<hr \/>\n<h3 id=\"3.%20%E5%BC%80%E5%A7%8B%E8%AE%AD%E7%BB%83\">3. \u5f00\u59cb\u8bad\u7ec3<\/h3>\n<p>\u6b64\u5904\u6709\u5176\u4f59\u95ee\u9898\u53ef\u53c2\u8003\u5b98\u65b9\u6587\u6863&#xff1a;\u8bad\u7ec3\u63a8\u7406 \u5b98\u65b9\u6587\u6863<\/p>\n<p>\u8be5\u4ee3\u7801\u4e0b\u53ef\u4ee5\u4fee\u6539 \u201cnum_epochs\u201d \u7684\u5927\u5c0f&#xff0c;500\u5c31\u5dee\u4e0d\u591a\u4e86&#xff1a;nnUNet\/nnunetv2\/training\/nnUNetTrainer\/nnUNetTrainer.py<\/p>\n<p>self.num_epochs &#061; 500 # 1000 <\/p>\n<p>\u5f00\u59cb\u8bad\u7ec3&#xff0c;\u7ec8\u7aef\u8f93\u5165\u5982\u4e0b\u547d\u4ee4&#xff0c;\u9009\u62e9\u5176\u4e2d\u4e00\u79cd\u5373\u53ef<\/p>\n<p>\u5f62\u5982 \u201c\u670d\u52a1\u5668\u5361\u53f7&#xff08;\u53ef\u7701\u7565&#xff09;\/ \u8bad\u7ec3 \/ \u6570\u636e\u96c6\u53f73\u4f4d \/ \u914d\u7f6e&#xff08;\u5982\u4e0b&#xff09; \/ flod&#061;0\u81f35\u201d<\/p>\n<p>nnUNet \u6709\u56db\u79cd\u914d\u7f6e&#xff1a;2D&#xff08;2d&#xff09;\u30013D \u5168\u5206\u8fa8\u7387&#xff08;3d_fullres&#xff09;\u30013D \u4f4e\u5206\u8fa8\u7387&#xff08;3d_lowres&#xff09;\u3001\u7ea7\u8054\u7684\u5168\u5206\u8fa8\u7387 U-Net&#xff08;3d_cascade_fullres&#xff09;<\/p>\n<p>CUDA_VISIBLE_DEVICES&#061;3 nnUNetv2_train 001 2d 0<\/p>\n<p>nnUNetv2_train 001 2d 0<br \/>\nnnUNetv2_train 001 2d 1<br \/>\nnnUNetv2_train 001 2d 2<br \/>\nnnUNetv2_train 001 2d 3<br \/>\nnnUNetv2_train 001 2d 4<\/p>\n<p>nnUNetv2_train 002 3d_lowres 4 <\/p>\n<p>\u6b64\u65f6&#xff0c;\u53ef\u4ee5\u770b\u5230\u6587\u4ef6\u5939\u66f4\u65b0\u4e86\u8bad\u7ec3\u7684\u5185\u5bb9&#xff0c;\u6b64\u5904\u6211\u53ea\u8dd1\u4e86001\u6570\u636e\u96c6\u7684 fold&#061;0 \u548c fold&#061;5&#xff0c;\u8fd8\u6709002\u6570\u636e\u96c6\u7684 fold&#061;4<\/p>\n<p class=\"img-center\"><img loading=\"lazy\" decoding=\"async\" alt=\"\" height=\"262\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2025\/07\/20250729165106-6888fbfa841c2.png\" width=\"274\" \/><\/p>\n<h3 id=\"4.%20%E9%AA%8C%E8%AF%81%E5%92%8C%E6%8E%A8%E7%90%86\">4. \u9a8c\u8bc1\u548c\u63a8\u7406<\/h3>\n<p>\u6267\u884c\u5982\u4e0b\u547d\u4ee4\u9a8c\u8bc1&#xff0c;2d\u6570\u636e\u96c6\u75282d&#xff0c;3d\u6570\u636e\u96c6\u75283d<\/p>\n<p>nnUNetv2_train 001 2d 0  &#8211;val &#8211;npz<br \/>\nnnUNetv2_train 001 2d 1  &#8211;val &#8211;npz<br \/>\nnnUNetv2_train 001 2d 2  &#8211;val &#8211;npz<br \/>\nnnUNetv2_train 001 2d 3  &#8211;val &#8211;npz<br \/>\nnnUNetv2_train 001 2d 4  &#8211;val &#8211;npz <\/p>\n<p>\u6b64\u65f6&#xff0c;nnUNet_results\u6587\u4ef6\u5939\u7684\u6bcf\u6298validation\u6587\u4ef6\u5939\u51fa\u73b0npz\u6587\u4ef6<\/p>\n<p class=\"img-center\"><img loading=\"lazy\" decoding=\"async\" alt=\"\" height=\"110\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2025\/07\/20250729165106-6888fbfa8d240.png\" width=\"153\" \/><\/p>\n<p>\u00a0\u6267\u884c\u5982\u4e0b\u547d\u4ee4&#xff0c;\u627e\u5bfb\u6700\u4f18\u6a21\u578b\u5e76\u7ed9\u51fa\u63a8\u7406\u547d\u4ee4&#xff0c;\u4e00\u5171\u5217\u4e863\u79cd&#xff0c;\u9009\u62e9\u9002\u5408\u81ea\u5df1\u7684\u4e00\u79cd\u5373\u53ef&#xff1a;<\/p>\n<p># \u5bf9\u6570\u636e\u96c6001\u5bfb\u627e\u6700\u4f73\u914d\u7f6e&#xff08;\u6d4b\u8bd52d\u914d\u7f6e&#xff09;<br \/>\nnnUNetv2_find_best_configuration 001 -c 2d<\/p>\n<p># \u5bf9\u6570\u636e\u96c6001\u5bfb\u627e\u6700\u4f73\u914d\u7f6e&#xff08;\u6d4b\u8bd52d\u914d\u7f6e,\u4ecefold&#061;1\u81f35\u4e2d\u627e&#xff09;<br \/>\nnnUNetv2_find_best_configuration 001 -c 2d -f 0 1 2 3 4<\/p>\n<p># \u5bf9\u6570\u636e\u96c6001\u5bfb\u627e\u6700\u4f73\u914d\u7f6e&#xff08;\u4f1a\u6d4b\u8bd5\u591a\u79cd\u914d\u7f6e&#xff09;<br \/>\nnnUNetv2_find_best_configuration 001 -c 2d 2d 3d_lowres 3d_fullres 3d_cascade_fullres <\/p>\n<p>\u63a5\u4e0b\u6765\u6d4b\u8bd5&#xff0c;\u9996\u5148\u521b\u5efa\u4e00\u4e2a\u6587\u4ef6\u5939\u653e\u8f93\u51fa\u7ed3\u679c&#xff0c;\u5982 \u201cnnUNet\/nnUNetFrame\/OUTPUT\u201d<\/p>\n<p>\u7ec8\u7aef\u8f93\u5165\u5982\u4e0b\u547d\u4ee4<\/p>\n<p>nnUNetv2_predict -f 0 &#8211;save_probabilities<\/p>\n<p>nnUNetv2_predict -d Dataset004_Hippocampus -i nnUNet_raw\\\\Dataset004_Hippocampus\\\\imagesTs -o hippocampus_2d_predict -f  0 1 2 3 4 -tr nnUNetTrainer -c 2d -p nnUNetPlans &#8211;save_probabilities<\/p>\n<p>nnUNetv2_predict -d 001 -i \/home\/Data\/lizican\/heart\/nnUNet\/nnUNetFrame\/DATASET\/nnUNet_raw\/Dataset001_ARCADE\/imagesTs -o \/home\/Data\/lizican\/heart\/nnUNet\/nnUNetFrame\/OUTPUT -f  0  -tr nnUNetTrainer -c 2d &#8211;save_probabilities <\/p>\n<p>\u7ed3\u675f\u540e&#xff0c;\u6587\u4ef6\u5939\u957f\u8fd9\u6837<\/p>\n<p class=\"img-center\"><img loading=\"lazy\" decoding=\"async\" alt=\"\" height=\"132\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2025\/07\/20250729165106-6888fbfa925f2.png\" width=\"156\" \/><\/p>\n<p>\u73b0\u5728\u5c31\u53ef\u4ee5\u7528\u8fd9\u4e2a\u751f\u6210\u7684.png\u6587\u4ef6\u548c\u539f\u59cb\u6807\u7b7e\u6587\u4ef6\u8ba1\u7b97Dice&#xff0c;\u53ef\u4ee5\u81ea\u5df1\u5199\u4e2a\u811a\u672c&#xff0c;\u5f53\u7136\u8fc7\u7a0b\u4f1a\u6bd4\u8f83\u7e41\u7410\u3002<\/p>\n<hr \/>\n<h3 id=\"4.%20%E5%85%B6%E4%BB%96\">5. \u5176\u4ed6<\/h3>\n<p>\u4e0b\u8ff0\u5185\u5bb9\u6211\u8fd8\u6ca1\u6709\u5b9e\u9645\u64cd\u4f5c\u8fc7<\/p>\n<p>\u6267\u884c\u5982\u4e0b\u547d\u4ee4\u4fdd\u5b58ensemble\u7b56\u7565<\/p>\n<p>nnUNetv2_ensemble -i \/nnUNet\/nnUNetFrame\/OUTPUT -o \/nnUNet\/nnUNetFrame\/OUTPUT\/ensemble -np 8 <\/p>\n<p>\u63a8\u7406\u5b8c\u6210\u540e\u8fdb\u884c\u540e\u5904\u7406&#xff0c;\u7ed3\u6784\u4e0e\u4e0a\u8ff0\u547d\u4ee4\u4e00\u81f4&#xff0c;\u5927\u5bb6\u6ce8\u610f\u597d\u81ea\u5df1\u7684\u8def\u5f84\u4e0d\u8981\u5199\u9519&#xff0c;\u5f97\u5230\u5bf9\u6d4b\u8bd5\u96c6\u7684\u5206\u5272\u7ed3\u679c<\/p>\n<p>nnUNetv2_apply_postprocessing -i \/nnUNet\/nnUNetFrame\/OUTPUT\/ensemble -o \/nnUNet\/nnUNetFrame\/OUTPUT\/processing -pp_pkl_file \/nnUNet\/nnUNetFrame\/DATASET\/nnUNet_results\/Dataset001_ARCADE\/nnUNetTrainer__nnUNetPlans__2d\/fold_0\/checkpoint_best.pth -np 8 -plans_json 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