BSI 22/30426016 DC:2022 Edition
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BS ISO/IEC 3532-2. Information technology. 3D Printing and scanning. Medical image-Based modelling – Part 2: Segmentation
Published By | Publication Date | Number of Pages |
BSI | 2022 | 33 |
PDF Catalog
PDF Pages | PDF Title |
---|---|
7 | Foreword |
8 | Introduction |
9 | 1 Scope 2 Normative references 3 Terms and definitions |
10 | 4 Abbreviations |
11 | 5 Objective of segmentation 5.1 Background 5.2 Related works |
12 | 6 Overall segmentation process 6.1 General 6.2 Step1: data preparation |
13 | 6.3 Step2: preprocessing for segmentation 6.4 Step3: annotation 6.5 Step4: selection of segmentation network model 6.6 Step5: performance evaluation 6.7 Step6: model deployment and running 6.8 Step7: post-processing for segmentation 7 Data preparation 7.1 General 7.2 Medical image 7.2.1 General 7.2.2 CT scan |
14 | 7.2.3 MRI scan 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 |
15 | 8.2 Intensity normalization 8.3 Spacing normalization |
16 | 9 Annotation 9.1 Data labeling 9.2 Pre-processing for annotation 9.3 Dataset management (training and testing) |
17 | 9.4 Augmentation 10 Selection of network model 10.1 General |
18 | 10.2 Input patch 11 Evaluation 11.1 General |
19 | 11.2 Evaluation metrics 11.3 Evaluation procedure |
20 | 12 Deployment and running 13 Post-processing for segmentation |
21 | Annex€A (informative) CT scanning conditions for orbital bone segmentation |
22 | Annex€B (informative) Characteristics of orbital bone segmentation from CT |
25 | Annex€C (informative) Deep learning techniques |
26 | Annex€D (informative) Considerations for overall segmentation performance |
31 | Bibliography |