{"id":69931,"date":"2026-11-14T22:07:28","date_gmt":"2026-11-14T14:07:28","guid":{"rendered":"https:\/\/www.biocloudservice.com\/wordpress\/?p=69931"},"modified":"2026-11-14T22:07:28","modified_gmt":"2026-11-14T14:07:28","slug":"cellpose-cyto3-diameter-instance-recall-synthetic","status":"publish","type":"post","link":"https:\/\/www.biocloudservice.com\/wordpress\/cellpose-cyto3-diameter-instance-recall-synthetic\/","title":{"rendered":"Cellpose \u7684\u5206\u5272\u56fe\u770b\u7740\u5f88\u51c6\uff0c\u4e3a\u4ec0\u4e48\u5341\u4e8c\u679a\u7ec6\u80de\u53ea\u627e\u56de\u4e00\u679a\uff1f"},"content":{"rendered":"
\n

\u4e00\u5f20\u5206\u5272\u793a\u4f8b\u56fe\u4e0a\uff0c\u8f6e\u5ed3\u7d27\u8d34\u7740\u7ec6\u80de\u8fb9\u7f18\u3002\u56fe\u65c1\u8fd8\u6807\u6ce8\u4e86\u5e73\u5747\u91cd\u53e0\u5ea6\u8d85\u8fc7\u96f6\u70b9\u4e5d\u3002\u8fd9\u6837\u7684\u622a\u56fe\u6709\u65f6\u4f1a\u9690\u85cf\u4e00\u4e2a\u91cd\u8981\u5206\u6bcd\uff0c\u6709\u591a\u5c11\u679a\u7ec6\u80de\u6839\u672c\u6ca1\u51fa\u73b0\u5728\u9884\u6d4b\u91cc\uff1f\u6211\u4eec\u7528\u5341\u4e8c\u4e2a\u5df2\u77e5\u8f6e\u5ed3\u7684\u6a21\u62df\u8367\u5149\u76ee\u6807\u8fd0\u884c Cellpose\uff0c\u5b9e\u9645\u5f97\u5230\u8fc7\u53ea\u68c0\u51fa\u4e00\u679a\u3001\u8fd9\u4e00\u679a\u91cd\u53e0\u5ea6\u5374\u8fbe\u5230 0.918 \u7684\u7ed3\u679c\u3002\u4e0b\u9762\u628a\u8fd9\u4e00\u6b21\u5bb9\u6613\u8bef\u8bfb\u7684\u8bc4\u4f30\u62c6\u5f00\uff0c\u65e2\u770b\u8bc6\u522b\u51fa\u7684\u5bf9\u8c61\u6709\u591a\u51c6\uff0c\u4e5f\u95ee\u6f0f\u6389\u4e86\u8c01\uff0c\u6700\u540e\u56de\u5230\u76f4\u5f84\u53c2\u6570\u600e\u6837\u5bf9\u5e94\u56fe\u50cf\u5c3a\u5ea6\u3002<\/p>\n

\u4e00\u5f20\u56fe\u81f3\u5c11\u8981\u505a\u50cf\u7d20\u548c\u5bf9\u8c61\u4e24\u79cd\u6838\u5bf9<\/h2>\n

\u5148\u7ed9\u6bcf\u4e2a\u771f\u5b9e\u5bf9\u8c61\u72ec\u7acb\u7f16\u53f7\uff0c\u4fdd\u5b58\u4e00\u4efd\u5b9e\u4f8b\u6807\u7b7e\u56fe\u3002\u9884\u6d4b\u7ed3\u679c\u540c\u6837\u8981\u4fdd\u7559\u6574\u6570\u5b9e\u4f8b\u7f16\u53f7\u3002\u5982\u679c\u5148\u628a\u6240\u6709\u975e\u96f6\u50cf\u7d20\u8f6c\u6210\u4e00\u4e2a\u6574\u4f53\u7684\u4e8c\u503c\u524d\u666f\uff0c\u4e24\u4e2a\u7d27\u6328\u7684\u7ec6\u80de\u5373\u4f7f\u88ab\u9519\u8bef\u5730\u5408\u6210\u4e00\u5757\uff0c\u6574\u5e45\u56fe\u7684\u524d\u666f\u9762\u79ef\u4ecd\u53ef\u80fd\u51e0\u4e4e\u4e0d\u53d8\u3002\u76f8\u53cd\uff0c\u4e00\u4e2a\u8f83\u5927\u7684\u7ec6\u80de\u82e5\u88ab\u5207\u6210\u4e24\u534a\uff0c\u524d\u666f\u50cf\u7d20\u53ef\u80fd\u4e5f\u6ca1\u6709\u660e\u663e\u51cf\u5c11\u3002\u5bf9\u7814\u7a76\u7ec6\u80de\u6570\u3001\u5355\u7ec6\u80de\u5f3a\u5ea6\u6216\u8005\u7ec6\u80de\u95f4\u8ddd\u79bb\u7684\u4efb\u52a1\uff0c\u8fd9\u4e24\u79cd\u9519\u8bef\u90fd\u6bd4\u201c\u8f6e\u5ed3\u5dee\u4e00\u4e24\u4e2a\u50cf\u7d20\u201d\u66f4\u8981\u7d27\u3002\u8bc4\u4ef7\u65f6\u5148\u679a\u4e3e\u9884\u6d4b\u5bf9\u8c61\u548c\u771f\u503c\u5bf9\u8c61\uff0c\u518d\u4e13\u95e8\u7edf\u8ba1\u5408\u5e76\u3001\u788e\u88c2\u3001\u6f0f\u68c0\u548c\u591a\u62a5\u3002<\/p>\n

\u5bf9\u5019\u9009\u5bf9\u8c61\u548c\u771f\u503c\u53ef\u4ee5\u8ba1\u7b97\u4ea4\u5e76\u6bd4\uff0c\u5373\u4e24\u4e2a\u63a9\u819c\u76f8\u4ea4\u7684\u50cf\u7d20\u6570\u9664\u4ee5\u5b83\u4eec\u5e76\u96c6\u7684\u50cf\u7d20\u6570\u3002\u82e5\u4e00\u4e2a\u9884\u6d4b\u4e0e\u4e24\u679a\u771f\u503c\u90fd\u6709\u91cd\u53e0\uff0c\u4e0d\u80fd\u628a\u5b83\u91cd\u590d\u7b97\u4f5c\u4e24\u4e2a\u6b63\u786e\u5bf9\u8c61\u3002\u6211\u4eec\u5148\u5728\u4e24\u5957\u7f16\u53f7\u4e4b\u95f4\u505a\u4e00\u5bf9\u4e00\u5206\u914d\uff0c\u4f7f\u603b\u4ea4\u5e76\u6bd4\u5c3d\u53ef\u80fd\u9ad8\uff0c\u518d\u4ee5\u96f6\u70b9\u4e94\u4e3a\u9608\u503c\u5224\u65ad\u54ea\u4e9b\u914d\u5bf9\u8fbe\u6807\u3002\u8fd9\u4e2a\u6307\u6807\u548c\u6574\u5e45\u56fe\u7684\u524d\u666f\u5360\u6bd4\u662f\u4e0d\u540c\u5c42\u6b21\u7684\u91cf\uff0c\u4e24\u79cd\u90fd\u53ef\u8ba1\u7b97\uff0c\u540d\u79f0\u548c\u5206\u6bcd\u8981\u5199\u6e05\u695a\u3002\u82e5\u53ea\u6709\u5f88\u5c11\u5bf9\u8c61\u914d\u5bf9\u6210\u529f\uff0c\u4ec5\u5728\u6210\u529f\u5bf9\u8c61\u4e2d\u6c42\u5747\u503c\u4f1a\u6392\u9664\u6240\u6709\u6f0f\u68c0\uff0c\u4ece\u800c\u7ed9\u4eba\u8f6e\u5ed3\u6bd4\u5b9e\u9645\u8986\u76d6\u60c5\u51b5\u66f4\u597d\u7684\u5370\u8c61\u3002<\/p>\n

\u5b9e\u8dd1\u7ecf\u9a8c\uff5c\u76ee\u6807\u3001\u56fe\u50cf\u548c\u9608\u503c\u76f8\u540c\uff0c\u53ea\u6539\u50cf\u7d20\u76f4\u5f84<\/h2>\n

\u8fd9\u6b21\u5b89\u88c5 Cellpose 3.1.1.2\uff0c\u4f7f\u7528\u4f5c\u8005\u53d1\u5e03\u7684 cyto3 \u6743\u91cd\uff0c\u4ee5 PyTorch 2.5.1 \u5728 CPU \u4e0a\u6267\u884c\u6a21\u578b\u63a8\u65ad\u3002\u9009\u62e9\u8fd9\u4e2a\u7ecf\u5178\u7248\u672c\u662f\u4e3a\u4e86\u5355\u72ec\u6838\u67e5\u76f4\u5f84\u53c2\u6570\uff0c\u672c\u6587\u6570\u503c\u5bf9\u5e94\u8fd9\u5957\u6a21\u578b\u548c\u63a5\u53e3\u3002\u8f93\u5165\u662f\u5bbd\u4e8c\u767e\u4e8c\u5341\u56db\u3001\u9ad8\u4e00\u767e\u4e5d\u5341\u4e8c\u50cf\u7d20\u7684\u5355\u901a\u9053\u6a21\u62df\u56fe\u3002\u4e09\u884c\u56db\u5217\u5171\u5341\u4e8c\u4e2a\u4e92\u4e0d\u91cd\u53e0\u7684\u692d\u5706\uff0c\u5404\u81ea\u7684\u6c34\u5e73\u548c\u5782\u76f4\u534a\u5f84\u7565\u6709\u4e0d\u540c\u3002\u6211\u4eec\u5728\u692d\u5706\u5185\u751f\u6210\u8f83\u4eae\u7684\u4fe1\u53f7\uff0c\u505a\u8f7b\u5ea6\u6a21\u7cca\u5e76\u52a0\u5165\u56fa\u5b9a\u968f\u673a\u79cd\u5b50\u7684\u566a\u58f0\uff0c\u540c\u65f6\u4fdd\u7559\u52a0\u566a\u524d\u7684\u5341\u4e8c\u4e2a\u6807\u51c6\u8f6e\u5ed3\u3002\u8bc4\u4f30\u7528\u8fd9\u4e2a\u5df2\u77e5\u771f\u503c\uff0c\u6ca1\u6709\u62ff\u5b83\u5145\u5f53\u771f\u5b9e\u7ec4\u7ec7\u7684\u4e13\u5bb6\u6807\u6ce8\u3002\u5bf9\u5e94\u7248\u672c\u7684 Cellpose \u6a21\u578b\u8bf4\u660e<\/a><\/p>\n

\u4e09\u6b21\u63a8\u65ad\u90fd\u7528\u76f8\u540c\u6743\u91cd\u3001\u76f8\u540c\u56fe\u50cf\u3001\u76f8\u540c\u7ec6\u80de\u6982\u7387\u548c\u6d41\u573a\u9608\u503c\uff0c\u76f4\u5f84\u4f9d\u6b21\u8bbe\u7f6e\u4e3a\u5341\u4e09\u3001\u4e8c\u5341\u516b\u548c\u516d\u5341\u50cf\u7d20\u3002\u5341\u4e09\u4e0e\u4e8c\u5341\u516b\u90fd\u8fd4\u56de\u5341\u4e8c\u4e2a\u5b9e\u4f8b\uff0c\u5341\u4e8c\u4e2a\u5bf9\u8c61\u5168\u90e8\u4e00\u5bf9\u4e00\u8fbe\u6807\uff0c\u5e73\u5747\u914d\u5bf9\u4ea4\u5e76\u6bd4\u5206\u522b\u7ea6\u4e3a 0.940 \u548c 0.940\u3002\u8bbe\u7f6e\u516d\u5341\u65f6\u53ea\u5f97\u5230\u4e00\u4e2a\u5b9e\u4f8b\uff0c\u4ecd\u80fd\u4e0e\u4e00\u4e2a\u771f\u503c\u8fbe\u5230\u7ea6 0.918 \u7684\u4ea4\u5e76\u6bd4\uff0c\u4f46\u5269\u4e0b\u5341\u4e00\u679a\u90fd\u6ca1\u6709\u5bf9\u5e94\u9884\u6d4b\u3002\u4e09\u6b21\u524d\u666f\u5360\u56fe\u50cf\u7684\u6bd4\u4f8b\u7ea6\u4e3a\u767e\u5206\u4e4b\u5341\u4e03\u70b9\u56db\u3001\u767e\u5206\u4e4b\u5341\u4e03\u70b9\u56db\u548c\u767e\u5206\u4e4b\u4e00\u70b9\u4e09\u3002\u5355\u770b\u6700\u540e\u7559\u4e0b\u7684\u4e00\u679a\uff0c\u65e0\u6cd5\u53d1\u73b0\u8986\u76d6\u9762\u79ef\u548c\u5b9e\u4f8b\u53ec\u56de\u540c\u65f6\u4e0b\u964d\u3002<\/p>\n

\n\n\n\n\n\n\n\n
\u8bbe\u5b9a\u76f4\u5f84<\/th>\n\u5b9e\u9645\u9884\u6d4b\u6570<\/th>\n\u8fbe\u6807\u914d\u5bf9 \/ \u771f\u503c<\/th>\n\u914d\u5bf9\u5bf9\u8c61\u7684\u5e73\u5747\u4ea4\u5e76\u6bd4<\/th>\n<\/tr>\n<\/thead>\n
13 \u50cf\u7d20<\/td>\n12<\/td>\n12 \/ 12<\/td>\n0.940<\/td>\n<\/tr>\n
28 \u50cf\u7d20<\/td>\n12<\/td>\n12 \/ 12<\/td>\n0.940<\/td>\n<\/tr>\n
60 \u50cf\u7d20<\/td>\n1<\/td>\n1 \/ 12<\/td>\n0.918<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n
\"\u53ea\u68c0\u51fa\u4e00\u679a\uff0c\u4e5f\u80fd\u6709
cyto3 \u6a21\u578b\u5b9e\u8dd1\uff5c\u5bf9\u540c\u4e00\u5f20\u56fe\u53ea\u6539\u53d8\u76f4\u5f84\u53c2\u6570\u3002<\/figcaption><\/figure>\n

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import numpy as np\r\nfrom cellpose import models\r\n\r\nmodel = models.CellposeModel(gpu=False, model_type="cyto3")\r\nfor diameter_px in (13, 28, 60):\r\n    mask, flows, styles = model.eval(image, diameter=diameter_px,\r\n        channels=[0, 0], flow_threshold=0.4, cellprob_threshold=0.0)\r\n    ids = np.unique(mask)\r\n    print(diameter_px, (ids > 0).sum(), (mask > 0).mean())\r\n<\/code><\/pre>\n
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