{"id":453535,"date":"2024-10-20T09:30:46","date_gmt":"2024-10-20T09:30:46","guid":{"rendered":"https:\/\/pdfstandards.shop\/product\/uncategorized\/bs-iso-iec-3532-22024\/"},"modified":"2024-10-26T17:38:44","modified_gmt":"2024-10-26T17:38:44","slug":"bs-iso-iec-3532-22024","status":"publish","type":"product","link":"https:\/\/pdfstandards.shop\/product\/publishers\/bsi\/bs-iso-iec-3532-22024\/","title":{"rendered":"BS ISO\/IEC 3532-2:2024"},"content":{"rendered":"
PDF Pages<\/th>\n | PDF Title<\/th>\n<\/tr>\n | ||||||
---|---|---|---|---|---|---|---|
2<\/td>\n | undefined <\/td>\n<\/tr>\n | ||||||
7<\/td>\n | Foreword <\/td>\n<\/tr>\n | ||||||
8<\/td>\n | Introduction <\/td>\n<\/tr>\n | ||||||
9<\/td>\n | 1 Scope 2 Normative references 3 Terms and definitions <\/td>\n<\/tr>\n | ||||||
11<\/td>\n | 4 Abbreviated terms 5 Objective of segmentation 5.1 Background <\/td>\n<\/tr>\n | ||||||
12<\/td>\n | 5.2 Types of segmentation methods 6 Overall segmentation process 6.1 General <\/td>\n<\/tr>\n | ||||||
13<\/td>\n | 6.2 Step 1: data preparation 6.3 Step 2: preprocessing for segmentation 6.4 Step 3: annotation 6.5 Step 4: selection of segmentation network model 6.6 Step 5: performance evaluation <\/td>\n<\/tr>\n | ||||||
14<\/td>\n | 6.7 Step 6: model deployment and running 6.8 Step 7: post-processing for segmentation 7 Data preparation 7.1 General 7.2 Medical image 7.2.1 General 7.2.2 CT scan 7.2.3 MR image <\/td>\n<\/tr>\n | ||||||
15<\/td>\n | 7.3 Preparation steps 7.3.1 General 7.3.2 Image acquisition 7.3.3 Image reconstruction 8 Preprocessing for segmentation 8.1 General <\/td>\n<\/tr>\n | ||||||
16<\/td>\n | 8.2 Intensity normalization 8.3 Spacing normalization <\/td>\n<\/tr>\n | ||||||
17<\/td>\n | 9 Annotation 9.1 Data labelling 9.2 Preprocessing for annotation <\/td>\n<\/tr>\n | ||||||
18<\/td>\n | 9.3 Dataset management (training and testing) 9.4 Augmentation 10 Selection of network model 10.1 General <\/td>\n<\/tr>\n | ||||||
19<\/td>\n | 10.2 Input patch 11 Evaluation 11.1 General <\/td>\n<\/tr>\n | ||||||
20<\/td>\n | 11.2 Evaluation metrics <\/td>\n<\/tr>\n | ||||||
21<\/td>\n | 11.3 Evaluation procedure 12 Deployment and running <\/td>\n<\/tr>\n | ||||||
22<\/td>\n | 13 Post-processing for segmentation <\/td>\n<\/tr>\n | ||||||
23<\/td>\n | Annex A (informative) CT scanning conditions for orbital bone segmentation <\/td>\n<\/tr>\n | ||||||
24<\/td>\n | Annex B (informative) Characteristics of orbital bone segmentation from CT <\/td>\n<\/tr>\n | ||||||
26<\/td>\n | Annex C (informative) Deep learning techniques <\/td>\n<\/tr>\n | ||||||
27<\/td>\n | Annex D (informative) Considerations for overall segmentation performance <\/td>\n<\/tr>\n | ||||||
32<\/td>\n | Bibliography <\/td>\n<\/tr>\n<\/table>\n","protected":false},"excerpt":{"rendered":" Information technology. Medical image-based modelling for 3D printing – Segmentation<\/b><\/p>\n |