Convolutional neural networks for segmentation and object detection of human semen

Malte Stær Nissen; Oswin Krause; Kristian Almstrup; Søren Kjærulff; Torben T. Nielsen; Mads Nielsen

doi:10.1007/978-3-319-59126-1_33

Convolutional neural networks for segmentation and object detection of human semen

Malte Stær Nissen^*, Oswin Krause, Kristian Almstrup, Søren Kjærulff, Torben T. Nielsen, Mads Nielsen

^*Corresponding author af dette arbejde

Datalogisk Institut

5 Citationer (Scopus)

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.

Originalsprog	Engelsk
Titel	Image Analysis : 20th Scandinavian Conference, SCIA 2017, Tromsø, Norway, June 12–14, 2017, Proceedings, Part I
Redaktører	Puneet Sharma, Filippo Maria Bianchi
Antal sider	10
Vol/bind	Part 1
Forlag	Springer
Publikationsdato	2017
Sider	397-406
ISBN (Trykt)	978-3-319-59125-4
ISBN (Elektronisk)	978-3-319-59126-1
DOI	https://doi.org/10.1007/978-3-319-59126-1_33
Status	Udgivet - 2017
Begivenhed	20th Scandinavian Conference on Image Analysis - Tromsø, Norge Varighed: 12 jun. 2017 → 14 jun. 2017 Konferencens nummer: 20

Konference

Konference	20th Scandinavian Conference on Image Analysis
Nummer	20
Land/Område	Norge
By	Tromsø
Periode	12/06/2017 → 14/06/2017

Navn	Lecture notes in computer science
Vol/bind	10269
ISSN	0302-9743

Adgang til dokumentet

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

https://arxiv.org/pdf/1704.00498.pdf

Andre filer og links

Link to publication in Scopus

Citationsformater

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

Convolutional neural networks for segmentation and object detection of human semen. / Nissen, Malte Stær; Krause, Oswin; Almstrup, Kristian et al.

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 › peer review

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

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). doi: 10.1007/978-3-319-59126-1_33

Nissen, Malte Stær ; Krause, Oswin ; Almstrup, Kristian et al. / 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).

@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",

note = "20th Scandinavian Conference on Image Analysis ; Conference date: 12-06-2017 Through 14-06-2017",

}

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

UR - http://www.scopus.com/inward/record.url?scp=85020400552&partnerID=8YFLogxK

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

T2 - 20th Scandinavian Conference on Image Analysis

Y2 - 12 June 2017 through 14 June 2017

ER -

Convolutional neural networks for segmentation and object detection of human semen

Abstract

Konference

Adgang til dokumentet

Andre filer og links

Fingeraftryk

Citationsformater