Convolutional neural networks for segmentation and object detection of human semen

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt

Standard

Convolutional neural networks for segmentation and object detection of human semen. / Nissen, Malte Stær; Krause, Oswin; Almstrup, Kristian; Kjærulff, Søren; Nielsen, Torben T.; Nielsen, Mads.

Image Analysis: 20th Scandinavian Conference, SCIA 2017, Tromsø, Norway, June 12–14, 2017, Proceedings, Part I. red. / Puneet Sharma; Filippo Maria Bianchi. Bind Part 1 Springer, 2017. s. 397-406 (Lecture notes in computer science, Bind 10269).

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt

Harvard

Nissen, MS, Krause, O, Almstrup, K, Kjærulff, S, Nielsen, TT & Nielsen, M 2017, Convolutional neural networks for segmentation and object detection of human semen. i P Sharma & FM Bianchi (red), Image Analysis: 20th Scandinavian Conference, SCIA 2017, Tromsø, Norway, June 12–14, 2017, Proceedings, Part I. bind Part 1, Springer, Lecture notes in computer science, bind 10269, s. 397-406, 20th Scandinavian Conference on Image Analysis, Tromsø, Norge, 12/06/2017. https://doi.org/10.1007/978-3-319-59126-1_33

APA

Nissen, M. S., Krause, O., Almstrup, K., Kjærulff, S., Nielsen, T. T., & Nielsen, M. (2017). Convolutional neural networks for segmentation and object detection of human semen. I P. Sharma, & F. M. Bianchi (red.), Image Analysis: 20th Scandinavian Conference, SCIA 2017, Tromsø, Norway, June 12–14, 2017, Proceedings, Part I (Bind Part 1, s. 397-406). Springer. Lecture notes in computer science Bind 10269 https://doi.org/10.1007/978-3-319-59126-1_33

Vancouver

Nissen MS, Krause O, Almstrup K, Kjærulff S, Nielsen TT, Nielsen M. Convolutional neural networks for segmentation and object detection of human semen. I Sharma P, Bianchi FM, red., Image Analysis: 20th Scandinavian Conference, SCIA 2017, Tromsø, Norway, June 12–14, 2017, Proceedings, Part I. Bind Part 1. Springer. 2017. s. 397-406. (Lecture notes in computer science, Bind 10269). https://doi.org/10.1007/978-3-319-59126-1_33

Author

Nissen, Malte Stær ; Krause, Oswin ; Almstrup, Kristian ; Kjærulff, Søren ; Nielsen, Torben T. ; Nielsen, Mads. / Convolutional neural networks for segmentation and object detection of human semen. Image Analysis: 20th Scandinavian Conference, SCIA 2017, Tromsø, Norway, June 12–14, 2017, Proceedings, Part I. red. / Puneet Sharma ; Filippo Maria Bianchi. Bind Part 1 Springer, 2017. s. 397-406 (Lecture notes in computer science, Bind 10269).

Bibtex

@inproceedings{0b46cbffddd74b58847e6e9a89c31b9d,
title = "Convolutional neural networks for segmentation and object detection of human semen",
abstract = "We compare a set of convolutional neural network (CNN) architectures for the task of segmenting and detecting human sperm cells in an image taken from a semen sample. In contrast to previous work, samples are not stained or washed to allow for full sperm quality analysis, making analysis harder due to clutter. Our results indicate that training on full images is superior to training on patches when class-skew is properly handled. Full image training including up-sampling during training proves to be beneficial in deep CNNs for pixel wise accuracy and detection performance. Predicted sperm cells are found by using connected components on the CNN predictions. We investigate optimization of a threshold parameter on the size of detected components. Our best network achieves 93.87% precision and 91.89% recall on our test dataset after thresholding outperforming a classical image analysis approach.",
keywords = "Convolutional neural networks, Deep learning, Fertility examination, Human sperm, Segmentation",
author = "Nissen, {Malte St{\ae}r} and Oswin Krause and Kristian Almstrup and S{\o}ren Kj{\ae}rulff and Nielsen, {Torben T.} and Mads Nielsen",
year = "2017",
doi = "10.1007/978-3-319-59126-1_33",
language = "English",
isbn = "978-3-319-59125-4",
volume = "Part 1",
series = "Lecture notes in computer science",
publisher = "Springer",
pages = "397--406",
editor = "Puneet Sharma and Bianchi, {Filippo Maria}",
booktitle = "Image Analysis",
address = "Switzerland",
note = "null ; Conference date: 12-06-2017 Through 14-06-2017",

}

RIS

TY - GEN

T1 - Convolutional neural networks for segmentation and object detection of human semen

AU - Nissen, Malte Stær

AU - Krause, Oswin

AU - Almstrup, Kristian

AU - Kjærulff, Søren

AU - Nielsen, Torben T.

AU - Nielsen, Mads

N1 - Conference code: 20

PY - 2017

Y1 - 2017

N2 - We compare a set of convolutional neural network (CNN) architectures for the task of segmenting and detecting human sperm cells in an image taken from a semen sample. In contrast to previous work, samples are not stained or washed to allow for full sperm quality analysis, making analysis harder due to clutter. Our results indicate that training on full images is superior to training on patches when class-skew is properly handled. Full image training including up-sampling during training proves to be beneficial in deep CNNs for pixel wise accuracy and detection performance. Predicted sperm cells are found by using connected components on the CNN predictions. We investigate optimization of a threshold parameter on the size of detected components. Our best network achieves 93.87% precision and 91.89% recall on our test dataset after thresholding outperforming a classical image analysis approach.

AB - We compare a set of convolutional neural network (CNN) architectures for the task of segmenting and detecting human sperm cells in an image taken from a semen sample. In contrast to previous work, samples are not stained or washed to allow for full sperm quality analysis, making analysis harder due to clutter. Our results indicate that training on full images is superior to training on patches when class-skew is properly handled. Full image training including up-sampling during training proves to be beneficial in deep CNNs for pixel wise accuracy and detection performance. Predicted sperm cells are found by using connected components on the CNN predictions. We investigate optimization of a threshold parameter on the size of detected components. Our best network achieves 93.87% precision and 91.89% recall on our test dataset after thresholding outperforming a classical image analysis approach.

KW - Convolutional neural networks

KW - Deep learning

KW - Fertility examination

KW - Human sperm

KW - Segmentation

U2 - 10.1007/978-3-319-59126-1_33

DO - 10.1007/978-3-319-59126-1_33

M3 - Article in proceedings

AN - SCOPUS:85020400552

SN - 978-3-319-59125-4

VL - Part 1

T3 - Lecture notes in computer science

SP - 397

EP - 406

BT - Image Analysis

A2 - Sharma, Puneet

A2 - Bianchi, Filippo Maria

PB - Springer

Y2 - 12 June 2017 through 14 June 2017

ER -

ID: 184142886