Explainable Weakly-Supervised Cell Segmentation by Canonical Shape Learning and Transformation

• Type: Article in International Conference Proceedings Book
• Date: 2022

Authors

Costa, P (Author) Other; Gaudio, A (Author) Other; Aurélio Campilho (Author) FEUP; Jaime S Cardoso (Author) FEUP

Conference proceedings International

Title: International Conference on Medical Imaging with Deep Learning, MIDL 2022, 6-8 July 2022, Zurich, Switzerland.

Pages: 250-260

5th International Conference on Medical Imaging with Deep Learning, MIDL 2022

Zurich, 6 July 2022 through 8 July 2022

Indexing

Scopus - 0 Citations

Other information

Authenticus ID: P-00X-XHP

Abstract (EN): Microscopy images have been increasingly analyzed quantitatively in biomedical research. Segmenting individual cell nucleus is an important step as many research studies involve counting cell nuclei and analysing their shape. We propose a novel weakly supervised instance segmentation method trained with image segmentation masks only. Our system comprises two models: an implicit shape Multi-Layer Perceptron (MLP) that learns the shape of the nuclei in canonical coordinates; and 2) an encoder that predicts the parameters of the affine transformation to deform the canonical shape into the correct location, scale, and orientation in the image. To further improve the performance of the model, we propose a loss that uses the total number of nuclei in an image as supervision. Our system is explainable, as the implicit shape MLP learns that the canonical shape of the cell nuclei is a circle, and interpretable as the output of the encoder are parameters of affine transformations. We obtain image segmentation performance close to DeepLabV3 and, additionally, obtain an F1-scoreIoU=0.5 of 68.47% at the instance segmentation task, even though the system was trained with image segmentations. © 2022 P. Costa, A. Gaudio, A. Campilho & J.S. Cardoso.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 10