Analysis and segmentation of KI-67 immunohistochemistry images for breast cancer diagnosis
For breast cancer diagnosis, the computation of the KI-67 score is a useful metric to define patient?s treatment. For a proper computation of this score, tumour and stroma areas have to be distinguished in KI-67 images. However, this is not an easy task due to the fact that in KI-67 images, tumour a...
| Autor: | |
|---|---|
| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/381078 |
| Acceso en línea: | https://hdl.handle.net/2117/381078 |
| Access Level: | acceso abierto |
| Palabra clave: | Breast--Cancer--Diagnosis Imaging systems in medicine Immunohistochemistry KI-67 FastCUT U-NET Breast Cancer Mama--Càncer--Diagnòstic Imatgeria mèdica Immunohistoquímica Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo |
| Sumario: | For breast cancer diagnosis, the computation of the KI-67 score is a useful metric to define patient?s treatment. For a proper computation of this score, tumour and stroma areas have to be distinguished in KI-67 images. However, this is not an easy task due to the fact that in KI-67 images, tumour and stroma regions have a similar appearance. The objective of this research is to develop a semantic segmentation model capable of separating these two regions in KI-67 images. As for training this segmentation model there is the need of ground truth masks, a large part of the project has been focused on the generation of these masks. Regarding the ground truth generation, in this research we present a way to generate KI-67 ground truth masks based on translating KI-67 images into the CK-19 domain and segment them using simple morphological operators and manual corrections. The image-to-image translation model used for the translation purpose is a FASTCUT model and the semantic segmentation model we trained is a U-Net. Moreover, we present a comparative study of the performance of the U-Net for three different fields of view scope. From this research, quite good results are obtained for both ground truth generation and semantic segmentation processes. |
|---|