Benchmarking of algorithms for 3D tissue reconstruction

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

Standard

Benchmarking of algorithms for 3D tissue reconstruction. / Kartasalo, Kimmo; Latonen, Leena; Visakorpi, Tapio; Nykter, Matti; Ruusuvuori, Pekka.

2016 IEEE International Conference on Image Processing (ICIP). IEEE, 2016. p. 2360-2364.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

Harvard

Kartasalo, K, Latonen, L, Visakorpi, T, Nykter, M & Ruusuvuori, P 2016, Benchmarking of algorithms for 3D tissue reconstruction. in 2016 IEEE International Conference on Image Processing (ICIP). IEEE, pp. 2360-2364, IEEE International Conference on Image Processing, 1/01/00. https://doi.org/10.1109/ICIP.2016.7532781

APA

Kartasalo, K., Latonen, L., Visakorpi, T., Nykter, M., & Ruusuvuori, P. (2016). Benchmarking of algorithms for 3D tissue reconstruction. In 2016 IEEE International Conference on Image Processing (ICIP) (pp. 2360-2364). IEEE. https://doi.org/10.1109/ICIP.2016.7532781

Vancouver

Kartasalo K, Latonen L, Visakorpi T, Nykter M, Ruusuvuori P. Benchmarking of algorithms for 3D tissue reconstruction. In 2016 IEEE International Conference on Image Processing (ICIP). IEEE. 2016. p. 2360-2364 https://doi.org/10.1109/ICIP.2016.7532781

Author

Kartasalo, Kimmo ; Latonen, Leena ; Visakorpi, Tapio ; Nykter, Matti ; Ruusuvuori, Pekka. / Benchmarking of algorithms for 3D tissue reconstruction. 2016 IEEE International Conference on Image Processing (ICIP). IEEE, 2016. pp. 2360-2364

Bibtex - Download

@inproceedings{6b77bda6675d46b18cd102dbe34e3aa5,

title = "Benchmarking of algorithms for 3D tissue reconstruction",

abstract = "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.",

keywords = "Benchmark testing, Image reconstruction, Image registration, Indexes, Measurement, Standards, Three-dimensional displays, 3D reconstruction, benchmark, digital pathology, histology",

author = "Kimmo Kartasalo and Leena Latonen and Tapio Visakorpi and Matti Nykter and Pekka Ruusuvuori",

year = "2016",

month = "8",

day = "19",

doi = "10.1109/ICIP.2016.7532781",

language = "English",

publisher = "IEEE",

pages = "2360--2364",

booktitle = "2016 IEEE International Conference on Image Processing (ICIP)",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - Benchmarking of algorithms for 3D tissue reconstruction

AU - Kartasalo, Kimmo

AU - Latonen, Leena

AU - Visakorpi, Tapio

AU - Nykter, Matti

AU - Ruusuvuori, Pekka

PY - 2016/8/19

Y1 - 2016/8/19

N2 - 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.

AB - 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.

KW - Benchmark testing

KW - Image reconstruction

KW - Image registration

KW - Indexes

KW - Measurement

KW - Standards

KW - Three-dimensional displays

KW - 3D reconstruction

KW - benchmark

KW - digital pathology

KW - histology

U2 - 10.1109/ICIP.2016.7532781

DO - 10.1109/ICIP.2016.7532781

M3 - Conference contribution

SP - 2360

EP - 2364

BT - 2016 IEEE International Conference on Image Processing (ICIP)

PB - IEEE

ER -

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