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1. 3D Domain Adaptive Instance Segmentation Via Cyclic Segmentation GANs

   https://doi.org/10.1109/JBHI.2023.3281332  

   Lauenburg, Leander; Lin, Zudi; Zhang, Ruihan; Santos, Márcia dos; Huang, Siyu; Arganda-Carreras, Ignacio; Boyden, Edward S.; Pfister, Hanspeter; Wei, Donglai (January 2023, IEEE Journal of Biomedical and Health Informatics)

   3D instance segmentation for unlabeled imaging modalities is a challenging but essential task as collecting expert annotation can be expensive and time-consuming. Existing works segment a new modality by either deploying pre-trained models optimized on diverse training data or sequentially conducting image translation and segmentation with two relatively independent networks. In this work, we propose a novel Cyclic Segmentation Generative Adversarial Network (CySGAN) that conducts image translation and instance segmentation simultaneously using a unified network with weight sharing. Since the image translation layer can be removed at inference time, our proposed model does not introduce additional computational cost upon a standard segmentation model. For optimizing CySGAN, besides the CycleGAN losses for image translation and supervised losses for the annotated source domain, we also utilize self-supervised and segmentation-based adversarial objectives to enhance the model performance by leveraging unlabeled target domain images. We benchmark our approach on the task of 3D neuronal nuclei segmentation with annotated electron microscopy (EM) images and unlabeled expansion microscopy (ExM) data. The proposed CySGAN outperforms pre-trained generalist models, feature-level domain adaptation models, and the baselines that conduct image translation and segmentation sequentially. Our implementation and the newly collected, densely annotated ExM zebrafish brain nuclei dataset, named NucExM, are publicly available at https://connectomics-bazaar.github.io/proj/CySGAN/index.html. 

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2. Contextual Guided Segmentation Framework for Semi-supervised Video Instance Segmentation

   https://doi.org/10.1007/s00138-022-01278-x  

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3. Market Segmentation Trees

   https://doi.org/10.1287/msom.2023.1195  

   Aouad, Ali; Elmachtoub, Adam N.; Ferreira, Kris J.; McNellis, Ryan (March 2023, Manufacturing & Service Operations Management)

   Problem definition: We seek to provide an interpretable framework for segmenting users in a population for personalized decision making. Methodology/results: We propose a general methodology, market segmentation trees (MSTs), for learning market segmentations explicitly driven by identifying differences in user response patterns. To demonstrate the versatility of our methodology, we design two new specialized MST algorithms: (i) choice model trees (CMTs), which can be used to predict a user’s choice amongst multiple options, and (ii) isotonic regression trees (IRTs), which can be used to solve the bid landscape forecasting problem. We provide a theoretical analysis of the asymptotic running times of our algorithmic methods, which validates their computational tractability on large data sets. We also provide a customizable, open-source code base for training MSTs in Python that uses several strategies for scalability, including parallel processing and warm starts. Finally, we assess the practical performance of MSTs on several synthetic and real-world data sets, showing that our method reliably finds market segmentations that accurately model response behavior. Managerial implications: The standard approach to conduct market segmentation for personalized decision making is to first perform market segmentation by clustering users according to similarities in their contextual features and then fit a “response model” to each segment to model how users respond to decisions. However, this approach may not be ideal if the contextual features prominent in distinguishing clusters are not key drivers of response behavior. Our approach addresses this issue by integrating market segmentation and response modeling, which consistently leads to improvements in response prediction accuracy, thereby aiding personalization. We find that such an integrated approach can be computationally tractable and effective even on large-scale data sets. Moreover, MSTs are interpretable because the market segments can easily be described by a decision tree and often require only a fraction of the number of market segments generated by traditional approaches. Disclaimer: This work was done prior to Ryan McNellis joining Amazon. Funding: This work was supported by the National Science Foundation [Grants CMMI-1763000 and CMMI-1944428]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2023.1195 . 

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4. Fusing RGBD Tracking and Segmentation Tree Sampling for Multi-Hypothesis Volumetric Segmentation

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   Price, Andrew; Huang, Kun; Berenson, Dmitry (January 2021, 2021 IEEE International Conference on Robotics and Automation (ICRA))
5. Depth-Aware Mirror Segmentation

   Mei, Haiyang; Dong, Bo; Dong, Wen; Peers, Pieter; Yang, Xin; Zhang, Qiang; Wei, Xiaopeng (June 2021, IEEE Conference on Computer Vision and Pattern Recognition)

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