| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_78160fe5924d399deb065f8cbc5ad347
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_25718bce5308affc5440ea1406762995 |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20084
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20081
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-30096
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-10088
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-30056 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-11
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V10-25
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-045
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-084
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-25
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-30 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-62
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-04
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-32
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-08
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-30
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-11 |
| filingDate |
2019-07-25-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_031ee9c80af290e3ecd47510fbb4dcfd
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_199b797bad96e01ca27e113c10f3c084
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_ff524c80b9ea77521a0ca982a50471b4
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_e1eef6b443399e258f5417fd729ed229
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_db8479b86b5e72d97a6dbb680bd156a9
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_7df805077331199f6a3d5aba083bbe0e |
| publicationDate |
2019-12-27-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
CN-110619635-A |
| titleOfInvention |
System and method for magnetic resonance image segmentation of hepatocellular carcinoma based on deep learning |
| abstract |
The invention discloses a deep learning-based magnetic resonance image segmentation system and method for hepatocellular carcinoma. The method includes: acquiring multi-sequence magnetic resonance imaging images of patients with hepatocellular carcinoma tumors; inputting the acquired multi-sequence magnetic resonance imaging images into deep fusion In the network model, thereby obtaining the result map of lesion segmentation; the deep fusion network model includes a deep convolutional network module and a multi-sequence fusion module, and the deep convolutional network module is divided into multiple sequence channels, and the multi-sequence fusion module uses It is used to fuse all sequence channels to process the processing results of multi-sequence MRI images. The present invention performs lesion segmentation on multi-sequence magnetic resonance imaging images by deep fusion of network models, which can obtain better segmentation effect and more accurate segmentation. The present invention can be widely used in the field of medical image processing as a deep learning-based hepatocellular carcinoma magnetic resonance image segmentation system and method. |
| isCitedBy |
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-113554728-B
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-112561868-A
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-113554728-A
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-112802046-B
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-112561868-B
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-112802046-A |
| priorityDate |
2019-07-25-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |