Neuronal soma segmentation plays a crucial role in neuroscience applications.However,the fine structure,such as boundaries,small-volume neuronal somata and fibers,are commonly present in cell images,which pose a chall...Neuronal soma segmentation plays a crucial role in neuroscience applications.However,the fine structure,such as boundaries,small-volume neuronal somata and fibers,are commonly present in cell images,which pose a challenge for accurate segmentation.In this paper,we propose a 3D semantic segmentation network for neuronal soma segmentation to address this issue.Using an encoding-decoding structure,we introduce a Multi-Scale feature extraction and Adaptive Weighting fusion module(MSAW)after each encoding block.The MSAW module can not only emphasize the fine structures via an upsampling strategy,but also provide pixel-wise weights to measure the importance of the multi-scale features.Additionally,a dynamic convolution instead of normal convolution is employed to better adapt the network to input data with different distributions.The proposed MSAW-based semantic segmentation network(MSAW-Net)was evaluated on three neuronal soma images from mouse brain and one neuronal soma image from macaque brain,demonstrating the efficiency of the proposed method.It achieved an F1 score of 91.8%on Fezf2-2A-CreER dataset,97.1%on LSL-H2B-GFP dataset,82.8%on Thy1-EGFP-Mline dataset,and 86.9%on macaque dataset,achieving improvements over the 3D U-Net model by 3.1%,3.3%,3.9%,and 2.3%,respectively.展开更多
Weed management plays a crucial role in increasing crop yields.Semantic segmentation,which classifies each pixel in an image captured by a camera into categories such as crops,weeds,and background,is a widely used met...Weed management plays a crucial role in increasing crop yields.Semantic segmentation,which classifies each pixel in an image captured by a camera into categories such as crops,weeds,and background,is a widely used method in this context.However,conventional semantic segmentation methods rely solely on pixel information within the camera's field of view(FOV),hindering their ability to detect weeds outside the visible area.This limitation can lead to incomplete weed removal and inefficient herbicide application.Incorporating information beyond the FOV in crop and weed segmentation is therefore essential for effective herbicide usage.Nevertheless,existing research on crop and weed segmentation has largely overlooked this limitation.To address this issue,we propose the knowledge distillation-based outpainting and semantic segmentation network(KDOSS-Net)for crop and weed images,a novel framework that enhances segmentation accuracy by leveraging information beyond the FOV.KDOSS-Net consists of two parts:the object prediction-guided outpainting and semantic segmentation network(OPOSS-Net),which serves as the teacher model by restoring areas outside the FOV and performing semantic segmentation,and the semantic segmentation without outpainting network(SSWO-Net),which serves as the student model,directly performing segmentation without outpainting.Through knowledge distillation(KD),the student model learns from the teacher's outputs,which results in a lightweight yet highly accurate segmentation network that is suitable for deployment on agricultural robots with limited computing power.Experiments on three public datasets-Rice seedling and weed,CWFID,and BoniRob-yielded mean intersection over union(mIOU)scores of 0.6315,0.7101,and 0.7524,respectively.These results demonstrate that KDOSS-Net achieves higher accuracy than existing state-of-the-art(SOTA)segmentation models while significantly reducing computational overhead.Furthermore,the weed information extracted using our method is automatically linked as input to the open-source large language and vision assistant(LLaVA),enabling the development of a system that recommends optimal herbicide strategies tailored to the detected weed class.展开更多
基金supported by the STI2030-Major-Projects(No.2021ZD0200104)the National Natural Science Foundations of China under Grant 61771437.
文摘Neuronal soma segmentation plays a crucial role in neuroscience applications.However,the fine structure,such as boundaries,small-volume neuronal somata and fibers,are commonly present in cell images,which pose a challenge for accurate segmentation.In this paper,we propose a 3D semantic segmentation network for neuronal soma segmentation to address this issue.Using an encoding-decoding structure,we introduce a Multi-Scale feature extraction and Adaptive Weighting fusion module(MSAW)after each encoding block.The MSAW module can not only emphasize the fine structures via an upsampling strategy,but also provide pixel-wise weights to measure the importance of the multi-scale features.Additionally,a dynamic convolution instead of normal convolution is employed to better adapt the network to input data with different distributions.The proposed MSAW-based semantic segmentation network(MSAW-Net)was evaluated on three neuronal soma images from mouse brain and one neuronal soma image from macaque brain,demonstrating the efficiency of the proposed method.It achieved an F1 score of 91.8%on Fezf2-2A-CreER dataset,97.1%on LSL-H2B-GFP dataset,82.8%on Thy1-EGFP-Mline dataset,and 86.9%on macaque dataset,achieving improvements over the 3D U-Net model by 3.1%,3.3%,3.9%,and 2.3%,respectively.
基金This work was supported in part by the Ministry of Science and ICT(MSIT),Korea,through the Information Technology Research Center(ITRC)Support Program under Grant IITP-2025-RS-2020-II201789in part by the Artificial Intelligence Convergence Innovation Human Resources Development Supervised by the Institute of Information&Communications Technology Planning&Evaluation(IITP)under Grant IITP-2025-RS-2023-00254592.
文摘Weed management plays a crucial role in increasing crop yields.Semantic segmentation,which classifies each pixel in an image captured by a camera into categories such as crops,weeds,and background,is a widely used method in this context.However,conventional semantic segmentation methods rely solely on pixel information within the camera's field of view(FOV),hindering their ability to detect weeds outside the visible area.This limitation can lead to incomplete weed removal and inefficient herbicide application.Incorporating information beyond the FOV in crop and weed segmentation is therefore essential for effective herbicide usage.Nevertheless,existing research on crop and weed segmentation has largely overlooked this limitation.To address this issue,we propose the knowledge distillation-based outpainting and semantic segmentation network(KDOSS-Net)for crop and weed images,a novel framework that enhances segmentation accuracy by leveraging information beyond the FOV.KDOSS-Net consists of two parts:the object prediction-guided outpainting and semantic segmentation network(OPOSS-Net),which serves as the teacher model by restoring areas outside the FOV and performing semantic segmentation,and the semantic segmentation without outpainting network(SSWO-Net),which serves as the student model,directly performing segmentation without outpainting.Through knowledge distillation(KD),the student model learns from the teacher's outputs,which results in a lightweight yet highly accurate segmentation network that is suitable for deployment on agricultural robots with limited computing power.Experiments on three public datasets-Rice seedling and weed,CWFID,and BoniRob-yielded mean intersection over union(mIOU)scores of 0.6315,0.7101,and 0.7524,respectively.These results demonstrate that KDOSS-Net achieves higher accuracy than existing state-of-the-art(SOTA)segmentation models while significantly reducing computational overhead.Furthermore,the weed information extracted using our method is automatically linked as input to the open-source large language and vision assistant(LLaVA),enabling the development of a system that recommends optimal herbicide strategies tailored to the detected weed class.
