Mauricio Murillo

Andrew Heschl, Mauricio Murillo, Keyhan Najafian et al.

This paper introduces a methodology for generating synthetic annotated data to address data scarcity in semantic segmentation tasks within the precision agriculture domain. Utilizing Denoising Diffusion Probabilistic Models (DDPMs) and Generative Adversarial Networks (GANs), we propose a dual diffusion model architecture for synthesizing realistic annotated agricultural data, without any human intervention. We employ super-resolution to enhance the phenotypic characteristics of the synthesized images and their coherence with the corresponding generated masks. We showcase the utility of the proposed method for wheat head segmentation. The high quality of synthesized data underscores the effectiveness of the proposed methodology in generating image-mask pairs. Furthermore, models trained on our generated data exhibit promising performance when tested on an external, diverse dataset of real wheat fields. The results show the efficacy of the proposed methodology for addressing data scarcity for semantic segmentation tasks. Moreover, the proposed approach can be readily adapted for various segmentation tasks in precision agriculture and beyond.

Sébastien Quetin, Andrew Heschl, Mauricio Murillo et al.

Purpose: To present a high-performing, robust, and flexible deep learning pipeline for automatic segmentation of 30 organs-at-risk (OARs) in head and neck (H&N) cancer patients, using MRI, CT, or both. Method: We trained a segmentation pipeline on paired CT and MRI-T1 scans from 296 patients. We combined data from the H&N OARs CT and MR segmentation (HaN-Seg) challenge and the Burdenko and GLIS-RT datasets from the Cancer Imaging Archive (TCIA). MRI was rigidly registered to CT, and both were stacked as input to an nnU-Net pipeline. Left and right OARs were merged into single classes during training and separated at inference time based on anatomical position. Modality Dropout was applied during the training, ensuring the model would learn from both modalities and robustly handle missing modalities during inference. The trained model was evaluated on the HaN-Seg test set and three TCIA datasets. Predictions were also compared with Limbus AI software. Dice Score (DS) and Hausdorff Distance (HD) were used as evaluation metrics. Results: The pipeline achieved state-of-the-art performance on the HaN-Seg challenge with a mean DS of 78.12% and HD of 3.42 mm. On TCIA datasets, the model maintained strong agreement with Limbus AI software (DS: 77.43% , HD: 3.27 mm), while also flagging low-quality contours. The pipeline can segment seamlessly from the CT, the MRI scan, or both. Conclusion: The proposed pipeline achieved the best DS and HD scores among all HaN-Seg challenge participants and establishes a new state-of-the-art for fully automated, multi-modal segmentation of H&N OARs.