Benchmarking of algorithms for 3D tissue reconstruction
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Yksityiskohdat
| Alkuperäiskieli | Englanti |
|---|---|
| Otsikko | 2016 IEEE International Conference on Image Processing (ICIP) |
| Kustantaja | IEEE |
| Sivut | 2360-2364 |
| Sivumäärä | 5 |
| ISBN (elektroninen) | 978-1-4673-9961-6 |
| DOI - pysyväislinkit | |
| Tila | Julkaistu - 19 elokuuta 2016 |
| OKM-julkaisutyyppi | A4 Artikkeli konferenssijulkaisussa |
| Tapahtuma | IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING - Kesto: 1 tammikuuta 1900 → … |
Julkaisusarja
| Nimi | |
|---|---|
| ISSN (elektroninen) | 2381-8549 |
Conference
| Conference | IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING |
|---|---|
| Ajanjakso | 1/01/00 → … |
Tiivistelmä
Studying tissue structure in 3D is beneficial in many applications. Reconstructing the structure based on histological sections has the advantages of high resolution and compatibility with conventional staining and interpretation techniques. However, obtaining an accurate 3D reconstruction based on a sequence of 2D sections is a difficult task. Evaluating the accuracy of such reconstructions is also challenging and it is often performed based only on visual inspections or a single indirect numerical measure. Here, we present a benchmarking framework composed of a panel of complementary metrics for assessing the quality of 3D reconstructions. We then apply the framework to evaluate the performance of several popular image registration algorithms in this context.