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Background: Sperm tail morphology and motility have been demonstrated to be important factors in determining sperm quality for in vitro fertilization. However, many existing computer-aided sperm analysis systems leave the sperm tail out of the analysis, as detecting a few tail pixels is challenging. Moreover, some publicly available datasets for classifying morphological defects contain images limited only to the sperm head. This study focuses on the segmentation of full sperm, which consists of the head and tail parts, and appear alone and in groups. Methods: We re-purpose the Feature Pyramid Network to ensemble an input image with multiple masks from state-of-the-art segmentation algorithms using a scale-specific cross-attention module. We normalize homogeneous backgrounds for improved training. The low field depth of microscopes blurs the images, easily confusing human raters in discerning minuscule sperm from large backgrounds. We thus propose evaluation protocols for scoring segmentation models trained on imbalanced data and noisy ground truth. Results: The neural ensembling of noisy segmentation masks outperforms all single, state-of-the-art segmen- tation algorithms in full sperm segmentation. Human raters agree more on the head than tail masks. The algorithms also segment the head better than the tail. Conclusions: The extensive evaluation of state-of-the-art segmentation algorithms shows that full sperm segmentation is challenging. We release the SegSperm dataset of images from Intracytoplasmic Sperm Injection procedures to spur further progress on full sperm segmentation with noisy and imbalanced ground truth. The dataset is publicly available at https://doi.org/10.34808/6wm7-1159
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Autorzy (5)
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Joanna Liss
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- Wersja publikacji
- Accepted albo Published Version
- DOI:
- Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1016/j.compbiomed.2023.107520
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Informacje szczegółowe
- Kategoria:
- Publikacja w czasopiśmie
- Typ:
- artykuły w czasopismach
- Opublikowano w:
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COMPUTERS IN BIOLOGY AND MEDICINE
nr 166,
ISSN: 0010-4825 - Język:
- angielski
- Rok wydania:
- 2023
- Opis bibliograficzny:
- Lewandowska E., Węsierski D., Mazur-Milecka M., Liss J., Węsierska A.: Ensembling noisy segmentation masks of blurred sperm images// COMPUTERS IN BIOLOGY AND MEDICINE -Vol. 166, (2023), s.107520-
- DOI:
- Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1016/j.compbiomed.2023.107520
- Źródła finansowania:
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- Publikacja bezkosztowa
- Weryfikacja:
- Politechnika Gdańska
wyświetlono 87 razy
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