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1. A deep learning-based strategy for producing dense 3D segmentations from sparsely annotated 2D images

   https://doi.org/10.1101/2024.06.14.599135  

   Thiyagarajan, Vijay Venu; Sheridan, Arlo; Harris, Kristen M; Manor, Uri (June 2024, bioRxiv)

   Producing dense 3D reconstructions from biological imaging data is a challenging instance segmentation task that requires significant ground-truth training data for effective and accurate deep learning-based models. Generating training data requires intense human effort to annotate each instance of an object across serial section images. Our focus is on the especially complicated brain neuropil, comprising an extensive interdigitation of dendritic, axonal, and glial processes visualized through serial section electron microscopy. We developed a novel deep learning-based method to generate dense 3D segmentations rapidly from sparse 2D annotations of a few objects on single sections. Models trained on the rapidly generated segmentations achieved similar accuracy as those trained on expert dense ground-truth annotations. Human time to generate annotations was reduced by three orders of magnitude and could be produced by non-expert annotators. This capability will democratize generation of training data for large image volumes needed to achieve brain circuits and measures of circuit strengths. 

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2. Weakly Supervised Deep Nuclei Segmentation using Points Annotation in Histopathology Images

   Qu, H; Wu, P.; Huang, Q.; Yi, J.; Riedlinger, G. M.; De, S.; Metaxas, D. (July 2019, Proceedings of Machine Learning Research)

   Nuclei segmentation is a fundamental task in histopathological image analysis. Typically, such segmentation tasks require significant effort to manually generate pixel-wise annotations for fully supervised training. To alleviate the manual effort, in this paper we propose a novel approach using points only annotation. Two types of coarse labels with complementary information are derived from the points annotation, and are then utilized to train a deep neural network. The fully- connected conditional random field loss is utilized to further refine the model without introducing extra computational complexity during inference. Experimental results on two nuclei segmentation datasets reveal that the proposed method is able to achieve competitive performance compared to the fully supervised counterpart and the state-of-the-art methods while requiring significantly less annotation effort. Our code is publicly available. 

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3. Self-Supervised Learning for Panoptic Segmentation of Multiple Fruit Flower Species

   https://doi.org/10.1109/LRA.2022.3217000  

   Siddique, Abubakar; Tabb, Amy; Medeiros, Henry (October 2022, IEEE Robotics and Automation Letters)

   Convolutional neural networks trained using manually generated labels are commonly used for semantic or instance segmentation. In precision agriculture, automated flower detection methods use supervised models and post-processing techniques that may not perform consistently as the appearance of the flowers and the data acquisition conditions vary. We propose a self-supervised learning strategy to enhance the sensitivity of segmentation models to different flower species using automatically generated pseudo-labels. We employ a data augmentation and refinement approach to improve the accuracy of the model predictions. The augmented semantic predictions are then converted to panoptic pseudo-labels to iteratively train a multi-task model. The self-supervised model predictions can be refined with existing post-processing approaches to further improve their accuracy. An evaluation on a multi-species fruit tree flower dataset demonstrates that our method outperforms state-of-the-art models without computationally expensive post-processing steps, providing a new baseline for flower detection applications. 

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4. An end-to-end recurrent compressed sensing method to denoise, detect and demix calcium imaging data

   https://doi.org/10.1038/s42256-024-00892-w  

   Zhang, Kangning; Tang, Sean; Zhu, Vivian; Barchini, Majd; Yang, Weijian (September 2024, Nature Machine Intelligence)

   Two-photon calcium imaging provides large-scale recordings of neuronal activities at cellular resolution. A robust, automated and high-speed pipeline to simultaneously segment the spatial footprints of neurons and extract their temporal activity traces while decontaminating them from background, noise and overlapping neurons is highly desirable to analyse calcium imaging data. Here we demonstrate DeepCaImX, an end-to-end deep learning method based on an iterative shrinkage-thresholding algorithm and a long short-term memory neural network to achieve the above goals altogether at a very high speed and without any manually tuned hyperparameter. DeepCaImX is a multi-task, multi-class and multi-label segmentation method composed of a compressed sensing-inspired neural network with a recurrent layer and fully connected layers. The neural network can simultaneously generate accurate neuronal footprints and extract clean neuronal activity traces from calcium imaging data. We trained the neural network with simulated datasets and benchmarked it against existing state-of-the-art methods with in vivo experimental data. DeepCaImX outperforms existing methods in the quality of segmentation and temporal trace extraction as well as processing speed. DeepCaImX is highly scalable and will benefit the analysis of mesoscale calcium imaging. 

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5. An Efficient Deep Representation Based Framework for Large-Scale Terrain Classification

   https://doi.org/10.1109/ICPR.2018.8545021  

   Yan, Yupeng; Rangarajan, Anand; Ranka, Sanjay (August 2018, International Conference on Pattern Recognition (ICPR))

   In this paper, we present a novel terrain classifica- tion framework for large-scale remote sensing images. A well- performing multi-scale superpixel tessellation based segmentation approach is employed to generate homogeneous and irregularly shaped regions, and a transfer learning technique is sequentially deployed to derive representative deep features by utilizing suc- cessful pre-trained convolutional neural network (CNN) models. This design is aimed to overcome the big problem of lacking available ground-truth data and to increase the generalization power of the multi-pixel descriptor. In the subsequent classification step, we train a fast and robust support vector machine (SVM) to assign the pixel-level labels. Its maximum-margin property can be easily combined with a graph Laplacian propagation approach. Moreover, we analyze the advantages of applying a feature selection technique to the deep CNN features which are extracted by transfer learning. In the experiments, we evaluate the whole framework based on different geographical types. Compared with other region-based classification methods, the results show that our framework can obtain state-of-the-art performance w.r.t. both classification accuracy and computational efficiency. 

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