CaltechAUTHORS
  A Caltech Library Service

Multiclass Weighted Loss for Instance Segmentation of Cluttered Cells

Guerrero-Peña, Fidel A. and Marrero Fernandez, Pedro D. and Ing Ren, Tsang and Yui, Mary and Rothenberg, Ellen and Cunha, Alexandre (2018) Multiclass Weighted Loss for Instance Segmentation of Cluttered Cells. In: 2018 25th IEEE International Conference on Image Processing (ICIP). IEEE , Piscataway, NJ, pp. 2451-2455. ISBN 9781479970612. http://resolver.caltech.edu/CaltechAUTHORS:20190201-143229124

[img] PDF - Submitted Version
See Usage Policy.

2479Kb

Use this Persistent URL to link to this item: http://resolver.caltech.edu/CaltechAUTHORS:20190201-143229124

Abstract

We propose a new multiclass weighted loss function for instance segmentation of cluttered cells. We are primarily motivated by the need of developmental biologists to quantify and model the behavior of blood T -cells which might help us in understanding their regulation mechanisms and ultimately help researchers in their quest for developing an effective immunotherapy cancer treatment. Segmenting individual touching cells in cluttered regions is challenging as the feature distribution on shared borders and cell foreground are similar thus difficulting discriminating pixels into proper classes. We present two novel weight maps applied to the weighted cross entropy loss function which take into account both class imbalance and cell geometry. Binary ground truth training data is augmented so the learning model can handle not only foreground and background but also a third touching class. This framework allows training using U - N et. Experiments with our formulations have shown superior results when compared to other similar schemes, outperforming binary class models with significant improvement of boundary adequacy and instance detection. We validate our results on manually annotated microscope images of T-cells.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/icip.2018.8451187DOIArticle
https://arxiv.org/abs/1802.07465arXivDiscussion Paper
ORCID:
AuthorORCID
Yui, Mary0000-0002-3136-2181
Rothenberg, Ellen0000-0002-3901-347X
Additional Information:© 2018 IEEE. We thank financial support from the Brazilian funding agencies FACEPE, CAPES and CNPq (FAG,PF, TIR), from the Beckman Institute at Caltech to the Center for Advanced Methods in Biological Image Analysis( AC), and thank the IBM Matching Grants Program for computer donation (AC).
Funders:
Funding AgencyGrant Number
Fundação do Amparo a Ciência e Tecnologia (FACEPE)UNSPECIFIED
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)UNSPECIFIED
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)UNSPECIFIED
Caltech Beckman InstituteUNSPECIFIED
IBMUNSPECIFIED
Subject Keywords:Deep learning, instance segmentation, multiclass segmentation, cell segmentation
Record Number:CaltechAUTHORS:20190201-143229124
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20190201-143229124
Official Citation:F. A. Guerrero-Peña, P. D. Marrero Fernandez, T. Ing Ren, M. Yui, E. Rothenberg and A. Cunha, "Multiclass Weighted Loss for Instance Segmentation of Cluttered Cells," 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, 2018, pp. 2451-2455. doi: 10.1109/ICIP.2018.8451187
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:92583
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:01 Feb 2019 23:18
Last Modified:01 Feb 2019 23:18

Repository Staff Only: item control page