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Shuo Han

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22 papers

2 author rows

AAMAS Conference 2026 Conference Paper

Active Inference through Incentive Design in Partially Observable Markov Decision Processes

Xinyi Wei
Chongyang Shi
Shuo Han
Ahmed Hemida
Charles A. Kamhoua
Jie Fu

Active inference refers to a class of methods that influence or control observed information to minimize uncertainty about latent or unknown variables. In this paper, we study a class of active inference problems in which an agent (the leader), with only partial observations, seeks to infer the unknown type of another agent (the follower), whose interactions with a dynamic environment are modeled as a Markov decision process (MDP). Different follower types are characterized by distinct dynamics, reward functions, or both, and each follower acts optimally to maximize its own reward. To improve inference accuracy and efficiency under imperfect observations, we introduce the paradigm of Active Inference through Incentive Design, wherein the leader strategically offers side payments (incentives) to elicit diverging observable behaviors from different follower types. This formulation leads to a leader–follower game in which the leader balances the trade-off between incentive cost and information gain, quantified by the entropy of the posterior distribution over follower types. We show that the resulting bi-level optimization problem can be reduced to a single-level one by leveraging the softmax temporal consistency between followers’ policies and value functions. This reduction enables an efficient first-order, gradient-basedalgorithm, wheregradientsarecomputed using observable operators from hidden Markov models. Experimental results in stochastic gridworld environments demonstrate that the proposed method significantly improves both the accuracy and efficiency of intent inference compared to systems without incentive mechanisms.

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AAAI Conference 2026 Conference Paper

ChatCLIDS: Simulating Persuasive AI Dialogues to Promote Closed-Loop Insulin Adoption in Type 1 Diabetes Care

Zonghai Yao
Talha Chafekar
Junda Wang
Shuo Han
Feiyun Ouyang
Junhui Qian
Lingxi Li
Hong Yu

Real-world adoption of closed-loop insulin delivery systems (CLIDS) in type 1 diabetes remains low, driven not by technical failure, but by diverse behavioral, psychosocial, and social barriers. We introduce ChatCLIDS, the first benchmark to rigorously evaluate LLM–driven persuasive dialogue for health behavior change. Our framework features a library of expert-validated virtual patients, each with clinically grounded, heterogeneous profiles and realistic adoption barriers, and simulates multi-turn interactions with nurse agents equipped with a diverse set of evidence-based persuasive strategies. ChatCLIDS uniquely supports longitudinal counseling and adversarial social influence scenarios, enabling robust, multi-dimensional evaluation. Our findings reveal that while larger and more reflective LLMs adapt strategies over time, all models struggle to overcome resistance, especially under realistic social pressure. These results highlight critical limitations of current LLMs for behavior change, and offer a high-fidelity, scalable testbed for advancing trustworthy persuasive AI in healthcare and beyond.

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AAAI Conference 2026 Conference Paper

Polysemic Semantic Instance Network for Cross-Modal Hashing

Shuo Han
Qibing Qin
Kezhen Xie
Wenfeng Zhang
Lei Huang

Hashing techniques are widely adopted in large-scale cross-modal retrieval due to their efficiency and low storage cost. However, semantic ambiguities, including polysemy, multi-object images, and missing semantic descriptions, significantly degrade the accuracy of alignment and retrieval performance. Most existing methods rely on one-to-one mappings that preserve only global average semantics, which fail to capture the intrinsic polysemous structures embedded within individual samples. To address this issue, we propose a novel Deep Polysemic Semantic Instance Hashing (DPSIH) method and design a Diverse Semantic Instance Embedding (DSIE) module. This module integrates local and global features through multi-head self-attention and residual learning, generating multiple diverse embeddings per sample to effectively capture fine-grained and polysemous semantic structures. Furthermore, we design a multi-embedding semantic correlation constraint that relaxes strict alignment restrictions to improve robustness under partial alignment, and introduce Maximum Mean Discrepancy (MMD) regularization to alleviate cross-modal distribution shifts. Additionally, an embedding diversity mechanism is proposed to prevent all embeddings from collapsing into a central or averaged representation, thereby enhancing semantic diversity. Extensive experiments on four benchmark datasets demonstrate that DPSIH significantly outperforms state-of-the-art methods and effectively improves the modeling of semantic ambiguity in cross-modal retrieval tasks.

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AAAI Conference 2026 Conference Paper

PRIME: Planning and Retrieval-Integrated Memory for Enhanced Reasoning

Hieu Tran
Zonghai Yao
Nguyen Luong Tran
Zhichao Yang
Feiyun Ouyang
Shuo Han
Razieh Rahimi
Hong Yu

Inspired by the dual-process theory of human cognition from Thinking, Fast and Slow, we introduce PRIME (Planning and Retrieval-Integrated Memory for Enhanced Reasoning), a multi-agent reasoning framework that dynamically integrates System 1 (fast, intuitive thinking) and System 2 (slow, deliberate thinking). PRIME first employs a Quick Thinking Agent to generate a rapid answer; if uncertainty is detected, it then triggers a structured System 2 reasoning pipeline composed of specialized agents for planning, hypothesis generation, retrieval, information integration, and decision-making. This multi-agent design mimics human cognitive processes faithfully and enhances both efficiency and accuracy. Experimental results with LLaMA 3 models demonstrate that PRIME enables open-source LLMs to perform competitively with state-of-the-art closed-source models like GPT-4 and GPT-4o on benchmarks requiring multi-hop and knowledge-grounded reasoning. This research establishes PRIME as a scalable solution for improving LLMs in domains requiring complex, knowledge-intensive reasoning.

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TMLR Journal 2025 Journal Article

Adaptive Incentive Design for Markov Decision Processes with Unknown Rewards

Haoxiang Ma
Shuo Han
Ahmed Hemida
Charles A kamhoua
Jie Fu

Incentive design, also known as model design or environment design for Markov decision processes(MDPs), refers to a class of problems in which a leader can incentivize his follower by modifying the follower's reward function, in anticipation that the follower's optimal policy in the resulting MDP can be desirable for the leader's objective. In this work, we propose gradient-ascent algorithms to compute the leader's optimal incentive design, despite the lack of knowledge about the follower's reward function. First, we formulate the incentive design problem as a bi-level optimization problem and demonstrate that, by the softmax temporal consistency between the follower's policy and value function, the bi-level optimization problem can be reduced to single-level optimization, for which a gradient-based algorithm can be developed to optimize the leader's objective. We establish several key properties of incentive design in MDPs and prove the convergence of the proposed gradient-based method. Next, we show that the gradient terms can be estimated from observations of the follower's best response policy, enabling the use of a stochastic gradient-ascent algorithm to compute a locally optimal incentive design without knowing or learning the follower's reward function. Finally, we analyze the conditions under which an incentive design remains optimal for two different rewards which are policy invariant. The effectiveness of the proposed algorithm is demonstrated using a small probabilistic transition system and a stochastic gridworld.

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ICLR Conference 2025 Conference Paper

ADIFF: Explaining audio difference using natural language

Soham Deshmukh
Shuo Han
Rita Singh
Bhiksha Raj

Understanding and explaining differences between audio recordings is crucial for fields like audio forensics, quality assessment, and audio generation. This involves identifying and describing audio events, acoustic scenes, signal characteristics, and their emotional impact on listeners. This paper stands out as the first work to comprehensively study the task of explaining audio differences and then propose benchmark, baselines for the task. First, we present two new datasets for audio difference explanation derived from the AudioCaps and Clotho audio captioning datasets. Using Large Language Models (LLMs), we generate three levels of difference explanations: (1) concise descriptions of audio events and objects, (2) brief sentences about audio events, acoustic scenes, and signal properties, and (3) comprehensive explanations that include semantics and listener emotions. For the baseline, we use prefix tuning where audio embeddings from two audio files are used to prompt a frozen language model. Our empirical analysis and ablation studies reveal that the naive baseline struggles to distinguish perceptually similar sounds and generate detailed tier 3 explanations. To address these limitations, we propose ADIFF, which introduces a cross-projection module, position captioning, and a three-step training process to enhance the model’s ability to produce detailed explanations. We evaluate our model using objective metrics and human evaluation and show our model enhancements lead to significant improvements in performance over naive baseline and SoTA Audio-Language Model (ALM) Qwen Audio. Lastly, we conduct multiple ablation studies to study the effects of cross-projection, language model parameters, position captioning, third stage fine-tuning, and present our findings. Our benchmarks, findings, and strong baseline pave the way for nuanced and human-like explanations of audio differences.

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AAAI Conference 2025 Conference Paper

Audio Entailment: Assessing Deductive Reasoning for Audio Understanding

Soham Deshmukh
Shuo Han
Hazim Bukhari
Benjamin Elizalde
Hannes Gamper
Rita Singh
Bhiksha Raj

Recent literature uses language to build foundation models for audio. These Audio-Language Models (ALMs) are trained on a vast number of audio-text pairs and show remarkable performance in tasks including Text-to-Audio Retrieval, Captioning, and Question Answering. However, their ability to engage in more complex open-ended tasks, like Interactive Question-Answering, requires proficiency in logical reasoning- a skill not yet benchmarked. We introduce the novel task of Audio Entailment to evaluate an ALM's deductive reasoning ability. This task assesses whether a text description (hypothesis) of audio content can be deduced from an audio recording (premise), with potential conclusions being entailment, neutral, or contradiction, depending on the sufficiency of the evidence. We create two datasets for this task with audio recordings sourced from two audio captioning datasets-AudioCaps and Clotho-and hypotheses generated using Large Language Models (LLMs). We benchmark state-of-the-art ALMs and find deficiencies in logical reasoning with both zero-shot and linear probe evaluations. Finally, we propose "caption-before-reason", an intermediate step of captioning that improves the Zero-Shot and linear-probe performance of ALMs by an absolute 6% and 3%, respectively.

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TMLR Journal 2025 Journal Article

D2 Actor Critic: Diffusion Actor Meets Distributional Critic

Lunjun Zhang
Shuo Han
Hanrui Lyu
Bradly C. Stadie

We introduce D2AC, a new model-free reinforcement learning (RL) algorithm designed to train expressive diffusion policies online effectively. At its core is a policy improvement objective that avoids the high variance of typical policy gradients and the complexity of backpropagation through time. This stable learning process is critically enabled by our second contribution: a robust distributional critic, which we design through a fusion of distributional RL and clipped double Q-learning. The resulting algorithm is highly effective, achieving state-of-the-art performance on a benchmark of eighteen hard RL tasks, including Humanoid, Dog, and Shadow Hand domains, spanning both dense-reward and goal-conditioned RL scenarios. Beyond standard benchmarks, we also evaluate a biologically motivated predator-prey task to examine the behavioral robustness and generalization capacity of our approach.

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ICML Conference 2025 Conference Paper

Of Mice and Machines: A Comparison of Learning Between Real World Mice and RL Agents

Shuo Han
German Espinosa
Junda Huang
Daniel A. Dombeck
Malcolm A. MacIver
Bradly C. Stadie

Recent advances in reinforcement learning (RL) have demonstrated impressive capabilities in complex decision-making tasks. This progress raises a natural question: how do these artificial systems compare to biological agents, which have been shaped by millions of years of evolution? To help answer this question, we undertake a comparative study of biological mice and RL agents in a predator-avoidance maze environment. Through this analysis, we identify a striking disparity: RL agents consistently demonstrate a lack of self-preservation instinct, readily risking “death” for marginal efficiency gains. These risk-taking strategies are in contrast to biological agents, which exhibit sophisticated risk-assessment and avoidance behaviors. Towards bridging this gap between the biological and artificial, we propose two novel mechanisms that encourage more naturalistic risk-avoidance behaviors in RL agents. Our approach leads to the emergence of naturalistic behaviors, including strategic environment assessment, cautious path planning, and predator avoidance patterns that closely mirror those observed in biological systems.

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JAIR Journal 2025 Journal Article

Robust Reward Design for Markov Decision Processes

Shuo Wu
Haoxiang Ma
Jie Fu
Shuo Han

The problem of reward design examines the interaction between a leader and a follower, where the leader aims to shape the follower’s behavior to maximize the leader’s payoff by modifying the follower’s reward function. Current approaches to reward design rely on an accurate model of how the follower responds to reward modifications, which can be sensitive to modeling inaccuracies. To address this issue of sensitivity, we present a solution that offers robustness against uncertainties in modeling the follower, including 1) how the follower breaks ties in the presence of nonunique best responses, 2) inexact knowledge of how the follower perceives reward modifications, and 3) bounded rationality of the follower. Our robust solution is guaranteed to exist under mild conditions and can be obtained numerically by solving a mixed-integer linear program. Numerical experiments on multiple test cases demonstrate that our solution improves robustness compared to the standard approach without incurring significant additional computing costs.

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AAMAS Conference 2024 Conference Paper

Covert Planning aganist Imperfect Observers

Haoxiang Ma
Chongyang Shi
Shuo Han
Michael R. Dorothy
Jie Fu

Covert planning refers to a class of constrained planning problems where an agent aims to accomplish a task with minimal information leaked to a passive observer to avoid detection. However, existing methods of covert planning often consider deterministic environments or do not exploit the observer’s imperfect information. This paper studies how covert planning can leverage the coupling of stochastic dynamics and the observer’s imperfect observation to achieve optimal task performance without being detected. Specifically, we employ a Markov decision process to model the interaction between the agent and its stochastic environment, and a partial observation function to capture the leaked information to a passive observer. Assuming the observer employs hypothesis testing to detect if the observation deviates from a nominal policy, the covert planning agent aims to maximize the total discounted reward while keeping the probability of being detected as an adversary below a given threshold. We prove that finite-memory policies are more powerful than Markovian policies in covert planning. Then, we develop a primal-dual proximal policy gradient method with a twotime-scale update to compute a (locally) optimal covert policy. We demonstrate the effectiveness of our methods using a stochastic gridworld example. Our experimental results illustrate that the proposed method computes a policy that maximizes the adversary’s expected reward without violating the detection constraint, and empirically demonstrates how the environmental noises can influence the performance of the covert policies.

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AAMAS Conference 2024 Conference Paper

Efficient Collaboration with Unknown Agents: Ignoring Similar Agents without Checking Similarity

Yansong Li
Shuo Han

Ad hoc teamwork (AHT) is concerned with developing an AI agent who learns to collaborate with different previously unseen partners. We consider a setting where the AI agent is provided with a hypothesis set of partners’ policies. Several online algorithms that take the hypothesis set as input can be applied to solve the AHT problem. One way to speed up these online learning algorithms is to eliminate the redundant policies, i. e. , partner models sharing the same collaborating policy, from the hypothesis set. Nevertheless, we show whether this elimination should be applied depends on the learning algorithm used by the AI agent. Specifically, we identify a property of a learning algorithm: redundancy-aware. When the learning algorithm is redundancy-aware, redundancy elimination is unnecessary. In other words, redundancy-aware algorithms can ignore similar agents in the hypothesis set. We demonstrate through an example that an online algorithm with redundancy-aware property exists when the hypothesis set contains the true partner policy. We test our approach on a team Markov game of two players. Comparative numerical analyses reveal that the redundancy-aware algorithm outperforms other standard no-regret learning algorithms including upper confidence bound (UCB), 𝑄-learning with UCB exploration, and the optimistic posterior sampling algorithm when the set of partner policies contains many redundant policies.

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JBHI Journal 2024 Journal Article

LoMAE: Simple Streamlined Low-Level Masked Autoencoders for Robust, Generalized, and Interpretable Low-Dose CT Denoising

Dayang Wang
Shuo Han
Yongshun Xu
Zhan Wu
Li Zhou
Bahareh Morovati
Hengyong Yu

Low-dose computed tomography (LDCT) offers reduced X-ray radiation exposure but at the cost of compromised image quality, characterized by increased noise and artifacts. Recently, transformer models emerged as a promising avenue to enhance LDCT image quality. However, the success of such models relies on a large amount of paired noisy and clean images, which are often scarce in clinical settings. In computer vision and natural language processing, masked autoencoders (MAE) have been recognized as a powerful self-pretraining method for transformers, due to their exceptional capability to extract representative features. However, the original pretraining and fine-tuning design fails to work in low-level vision tasks like denoising. In response to this challenge, we redesign the classical encoder-decoder learning model and facilitate a simple yet effective streamlined low-level vision MAE, referred to as LoMAE, tailored to address the LDCT denoising problem. Moreover, we introduce an MAE-GradCAM method to shed light on the latent learning mechanisms of the MAE/LoMAE. Additionally, we explore the LoMAE's robustness and generability across a variety of noise levels. Experimental findings show that the proposed LoMAE enhances the denoising capabilities of the transformer and substantially reduce their dependency on high-quality, ground-truth data. It also demonstrates remarkable robustness and generalizability over a spectrum of noise levels. In summary, the proposed LoMAE provides promising solutions to the major issues in LDCT including interpretability, ground truth data dependency, and model robustness/generalizability.

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NeurIPS Conference 2024 Conference Paper

Soft-Label Integration for Robust Toxicity Classification

Zelei Cheng
Xian Wu
Jiahao Yu
Shuo Han
Xin-Qiang Cai
Xinyu Xing

Toxicity classification in textual content remains a significant problem. Data with labels from a single annotator fall short of capturing the diversity of human perspectives. Therefore, there is a growing need to incorporate crowdsourced annotations for training an effective toxicity classifier. Additionally, the standard approach to training a classifier using empirical risk minimization (ERM) may fail to address the potential shifts between the training set and testing set due to exploiting spurious correlations. This work introduces a novel bi-level optimization framework that integrates crowdsourced annotations with the soft-labeling technique and optimizes the soft-label weights by Group Distributionally Robust Optimization (GroupDRO) to enhance the robustness against out-of-distribution (OOD) risk. We theoretically prove the convergence of our bi-level optimization algorithm. Experimental results demonstrate that our approach outperforms existing baseline methods in terms of both average and worst-group accuracy, confirming its effectiveness in leveraging crowdsourced annotations to achieve more effective and robust toxicity classification.

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TMLR Journal 2024 Journal Article

What is the Solution for State-Adversarial Multi-Agent Reinforcement Learning?

Songyang Han
Sanbao Su
Sihong He
Shuo Han
Haizhao Yang
Shaofeng Zou
Fei Miao

Various methods for Multi-Agent Reinforcement Learning (MARL) have been developed with the assumption that agents' policies are based on accurate state information. However, policies learned through Deep Reinforcement Learning (DRL) are susceptible to adversarial state perturbation attacks. In this work, we propose a State-Adversarial Markov Game (SAMG) and make the first attempt to investigate different solution concepts of MARL under state uncertainties. Our analysis shows that the commonly used solution concepts of optimal agent policy and robust Nash equilibrium do not always exist in SAMGs. To circumvent this difficulty, we consider a new solution concept called robust agent policy, where agents aim to maximize the worst-case expected state value. We prove the existence of robust agent policy for finite state and finite action SAMGs. Additionally, we propose a Robust Multi-Agent Adversarial Actor-Critic (RMA3C) algorithm to learn robust policies for MARL agents under state uncertainties. Our experiments demonstrate that our algorithm outperforms existing methods when faced with state perturbations and greatly improves the robustness of MARL policies. Our code is public on https://songyanghan.github.io/what_is_solution/.

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AAMAS Conference 2023 Conference Paper

Optimal Decoy Resource Allocation for Proactive Defense in Probabilistic Attack Graphs

Haoxiang Ma
Shuo Han
Nandi Leslie
Charles Kamhoua
Jie Fu

This paper investigates the problem of synthesizing proactive defense systems in which the defender can allocate deceptive targets and modify the cost of actions for the attacker who aims to compromise security assets in this system. We model the interaction of the attacker and the system using a formal security model– a probabilistic attack graph. By allocating fake targets/decoys, the defender aims to distract the attacker from compromising true targets. By increasing the cost of some attack actions, the defender aims to discourage the attacker from committing to certain policies and thereby improve the defense. To optimize the defense given limited decoy resources and operational constraints, we formulate the synthesis problem as a bi-level optimization problem, while the defender designs the system, in anticipation of the attacker’s best response given that the attacker has disinformation about the system due to the use of deception. Though the general formulation with bi-level optimization is NP-hard, we show that under certain assumptions, the problem can be transformed into a constrained optimization problem. We proposed an algorithm to approximately solve this constrained optimization problem using a novel, incentive-design method for projected gradient ascent. We demonstrate the effectiveness of the proposed method using numerical experiments.

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AAMAS Conference 2023 Conference Paper

Quantitative Planning with Action Deception in Concurrent Stochastic Games

Chongyang Shi
Shuo Han
Jie Fu

We study a class of two-player competitive concurrent stochastic games on graphs with reachability objectives. Specifically, player 1 aims to reach a subset 𝐹1 of game states, and player 2 aims to reach a subset 𝐹2 of game states where 𝐹2 ∩ 𝐹1 = ∅. Both players aim to satisfy their reachability objectives before their opponent does. Yet, the information players have about the game dynamics is asymmetric: P1 has a (set of) hidden actions unknown to P2 at the beginning of their interaction. In this setup, we investigate P1’s strategic planning of action deception that decides when to deviate from the Nash equilibrium in P2’s game model and employ a hidden action, so that P1 can maximize the value of action deception, which is the additional payoff compared to P1’s payoff in the game where P2 has complete information. Anticipating that P2 may detect his misperception about the game and adapt his strategy during interaction in unpredictable ways, we construct a planning problem for P1 to augment the game model with an incomplete model about the theory of mind of the opponent P2. While planning in the augmented game, P1 can effectively influence P2’s perception so as to entice P2 to take actions that benefit P1. We prove that the proposed deceptive planning algorithm maximizes a lower bound on the value of action deception and demonstrate the effectiveness of our deceptive planning algorithm using a robot motion planning problem inspired by soccer games.

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TMLR Journal 2023 Journal Article

Robust Multi-Agent Reinforcement Learning with State Uncertainty

Sihong He
Songyang Han
Sanbao Su
Shuo Han
Shaofeng Zou
Fei Miao

In real-world multi-agent reinforcement learning (MARL) applications, agents may not have perfect state information (e.g., due to inaccurate measurement or malicious attacks), which challenges the robustness of agents' policies. Though robustness is getting important in MARL deployment, little prior work has studied state uncertainties in MARL, neither in problem formulation nor algorithm design. Motivated by this robustness issue and the lack of corresponding studies, we study the problem of MARL with state uncertainty in this work. We provide the first attempt to the theoretical and empirical analysis of this challenging problem. We first model the problem as a Markov Game with state perturbation adversaries (MG-SPA) by introducing a set of state perturbation adversaries into a Markov Game. We then introduce robust equilibrium (RE) as the solution concept of an MG-SPA. We conduct a fundamental analysis regarding MG-SPA such as giving conditions under which such a robust equilibrium exists. Then we propose a robust multi-agent Q-learning (RMAQ) algorithm to find such an equilibrium, with convergence guarantees. To handle high-dimensional state-action space, we design a robust multi-agent actor-critic (RMAAC) algorithm based on an analytical expression of the policy gradient derived in the paper. Our experiments show that the proposed RMAQ algorithm converges to the optimal value function; our RMAAC algorithm outperforms several MARL and robust MARL methods in multiple multi-agent environments when state uncertainty is present. The source code is public on https://github.com/sihongho/robust_marl_with_state_uncertainty.

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YNIMG Journal 2020 Journal Article

Automatic cerebellum anatomical parcellation using U-Net with locally constrained optimization

Shuo Han
Aaron Carass
Yufan He
Jerry L. Prince

The cerebellum plays a central role in sensory input, voluntary motor action, and many neuropsychological functions and is involved in many brain diseases and neurological disorders. Cerebellar parcellation from magnetic resonance images provides a way to study regional cerebellar atrophy and also provides an anatomical map for functional imaging. In a recent comparison, a multi-atlas approach proved to be superior to other parcellation methods including some based on convolutional neural networks (CNNs) which have a considerable speed advantage. In this work, we developed an alternative CNN design for cerebellar parcellation, yielding a method that achieves the leading performance to date. The proposed method was evaluated on multiple data sets to show its broad applicability, and a Singularity container has been made publicly available.

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YNIMG Journal 2020 Journal Article

Longitudinal analysis of regional cerebellum volumes during normal aging

Shuo Han
Yang An
Aaron Carass
Jerry L. Prince
Susan M. Resnick

Some cross-sectional studies suggest reduced cerebellar volumes with aging, but there have been few longitudinal studies of age changes in cerebellar subregions in cognitively healthy older adults. In this work, 2, 023 magnetic resonance (MR) images of 822 cognitively normal participants from the Baltimore Longitudinal Study of Aging (BLSA) were analyzed. Participants ranged in age from 50 to 95 years (mean 70. 7 years) at the baseline assessment. Follow-up intervals were 1–9 years (mean 3. 7 years) for participants with two or more visits. We used a recently developed cerebellum parcellation algorithm based on convolutional neural networks to divide the cerebellum into 28 subregions. Linear mixed effects models were applied to the volume of each cerebellar subregion to investigate cross-sectional and longitudinal age effects, as well as effects of sex and their interactions, after adjusting for intracranial volume. Our findings suggest spatially varying atrophy patterns across the cerebellum with respect to age and sex both cross-sectionally and longitudinally.

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YNICL Journal 2019 Journal Article

Brain ventricle parcellation using a deep neural network: Application to patients with ventriculomegaly

Muhan Shao
Shuo Han
Aaron Carass
Xiang Li
Ari M. Blitz
Jaehoon Shin
Jerry L. Prince
Lotta M. Ellingsen

Numerous brain disorders are associated with ventriculomegaly, including both neuro-degenerative diseases and cerebrospinal fluid disorders. Detailed evaluation of the ventricular system is important for these conditions to help understand the pathogenesis of ventricular enlargement and elucidate novel patterns of ventriculomegaly that can be associated with different diseases. One such disease is normal pressure hydrocephalus (NPH), a chronic form of hydrocephalus in older adults that causes dementia. Automatic parcellation of the ventricular system into its sub-compartments in patients with ventriculomegaly is quite challenging due to the large variation of the ventricle shape and size. Conventional brain labeling methods are time-consuming and often fail to identify the boundaries of the enlarged ventricles. We propose a modified 3D U-Net method to perform accurate ventricular parcellation, even with grossly enlarged ventricles, from magnetic resonance images (MRIs). We validated our method on a data set of healthy controls as well as a cohort of 95 patients with NPH with mild to severe ventriculomegaly and compared with several state-of-the-art segmentation methods. On the healthy data set, the proposed network achieved mean Dice similarity coefficient (DSC) of 0.895 ± 0.03 for the ventricular system. On the NPH data set, we achieved mean DSC of 0.973 ± 0.02, which is significantly (p < 0.005) higher than four state-of-the-art segmentation methods we compared with. Furthermore, the typical processing time on CPU-base implementation of the proposed method is 2 min, which is much lower than the several hours required by the other methods. Results indicate that our method provides: 1) highly robust parcellation of the ventricular system that is comparable in accuracy to state-of-the-art methods on healthy controls; 2) greater robustness and significantly more accurate results on cases of ventricular enlargement; and 3) a tool that enables computation of novel imaging biomarkers for dilated ventricular spaces that characterize the ventricular system.

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YNIMG Journal 2018 Journal Article

Comparing fully automated state-of-the-art cerebellum parcellation from magnetic resonance images

Aaron Carass
Jennifer L. Cuzzocreo
Shuo Han
Carlos R. Hernandez-Castillo
Paul E. Rasser
Melanie Ganz
Vincent Beliveau
Jose Dolz

The human cerebellum plays an essential role in motor control, is involved in cognitive function (i. e. , attention, working memory, and language), and helps to regulate emotional responses. Quantitative in-vivo assessment of the cerebellum is important in the study of several neurological diseases including cerebellar ataxia, autism, and schizophrenia. Different structural subdivisions of the cerebellum have been shown to correlate with differing pathologies. To further understand these pathologies, it is helpful to automatically parcellate the cerebellum at the highest fidelity possible. In this paper, we coordinated with colleagues around the world to evaluate automated cerebellum parcellation algorithms on two clinical cohorts showing that the cerebellum can be parcellated to a high accuracy by newer methods. We characterize these various methods at four hierarchical levels: coarse (i. e. , whole cerebellum and gross structures), lobe, subdivisions of the vermis, and the lobules. Due to the number of labels, the hierarchy of labels, the number of algorithms, and the two cohorts, we have restricted our analyses to the Dice measure of overlap. Under these conditions, machine learning based methods provide a collection of strategies that are efficient and deliver parcellations of a high standard across both cohorts, surpassing previous work in the area. In conjunction with the rank-sum computation, we identified an overall winning method.

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