Evaluating resolution requirements for subtle caenorhabditis elegans strain discrimination using classical descriptors and CNN transformer models
[EN] Accurately distinguishing subtle phenotypic differences between Caenorhabditis elegans strains remains a major challenge in functional genetics and behavioural studies. Here, we evaluate how image resolution affects strain discrimination using an automated Multiview system combining macroscopic...
| Autores: | , , , , |
|---|---|
| Tipo de documento: | artigo |
| Data de publicação: | 2026 |
| País: | España |
| Recursos: | Universitat Politècnica de València (UPV) |
| Repositório: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglês |
| OAI Identifier: | oai:dnet:riunet______::c9a8452f9372b552aa357057acab02ab |
| Acesso em linha: | https://riunet.upv.es/handle/10251/235740 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Convolutional neural networks Phenotypic differentiation Deep learning Statistical analysis C. elegans |
| Resumo: | [EN] Accurately distinguishing subtle phenotypic differences between Caenorhabditis elegans strains remains a major challenge in functional genetics and behavioural studies. Here, we evaluate how image resolution affects strain discrimination using an automated Multiview system combining macroscopic plate-level imaging with high-resolution microscopic single-worm recordings. Three strains were analysed: wild-type N2, a transgenic line with mild dysfunction of GABAergic neurons leading to subtle locomotor alterations (vltIs66), and a strongly uncoordinated mutant strain (unc-1(vlt10)). The three strains were analysed using traditional locomotion and shape descriptors. While both imaging modalities detected clear differences for the strongly uncoordinated unc-1(vlt10) strain, no traditional morphometric and kinematic descriptors reliably separated vltIs66 from N2. We then trained a CNN¿Transformer directly on image sequences. When trained on macro-camera data, the model failed to discriminate between N2 and vltIs66, whereas the same architecture trained on micro-camera sequences achieved robust separation, revealing phenotype-specific patterns not captured by conventional descriptors. To quantify the impact of spatial detail, micro-camera recordings were progressively downscaled by factors of 2, 4, 8 and 16 and the model was retrained at each effective resolution. This resolution sweep showed that performance remains stable under moderate downsampling but degrades markedly at coarse resolutions, indicating a minimum effective pixel density required for subtle phenotype classification. These findings highlight the importance of high-resolution, sequence-based deep learning for detecting fine locomotor differences in C. elegans. |
|---|