Naslov
Cell nuclei segmentation using distance map regression and inverted Huber loss

Autori
Šarić, Matko ; Russo, Mladen ; Stella Maja ; Sikora Marjan

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings of 2022 7th International Conference on Smart and Sustainable Technologies (SpliTech) / - , 2022, 1-5

Skup
7th International Conference on Smart and Sustainable Technologies (SpliTech)

Mjesto i datum
Bol, Hrvatska; Split, Hrvatska, 05.07.2022. - 08.07.2022

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
deep learning ; cell nuclei segmentation ; digital pathology ; distance map regression
(duboko učenje ; segmentacija jezgri stanica ; digitalna patologija ; regresija mape udaljenosti)

Sažetak
Digital pathology gives opportunity for automatic analysis of tissue sample images aiming to produce quantitative profiles that could be exploited for diagnosis and treatment decisions. One of the most important steps in the tissue analysis is segmentation of cell nuclei. This task is challenging because of large variability of nuclear morphological features and wide presence of nuclear clusters that leads to merged instances. In this paper we propose cell nuclei segmentation method utilizing distance map regression to address the problem of touching nuclei. Our main contribution is a novel loss function created by modification of Huber loss. The proposed loss demonstrates better performance compared to other commonly used loss functions, while the proposed method outperforms other approaches that have similar complexity of neural network architecture.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo

POVEZANOST RADA