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arXiv

Volume Transformer: Revisiting Vanilla Transformers for 3D Scene Understanding

Kadir Yilmaz, Adrian Kruse, Tristan Höfer, Daan de Geus, Bastian Leibe

Transformers have become a common foundation across deep learning, yet 3D scene understanding still relies on specialized backbones with strong domain priors. This isolates the field from the broader Transformer ecosystem, limiting the transfer of research advances from other domains and the benefits of increasingly optimized software and hardware stacks. To bridge this gap, we propose the Volume Transformer (Volt), which adapts the vanilla Transformer encoder to 3D scenes with minimal modifications. Specifically, Volt partitions 3D scenes into volumetric patch tokens, processes them with full global self-attention, and injects positional information via 3D rotary positional embeddings (RoPE). Our initial experiments reveal that naively training Volt on standard 3D benchmarks leads to poor generalization, highlighting the limited scale of current 3D supervision. To overcome this, we introduce a data-efficient training recipe based on strong 3D augmentations, regularization, and distillation from a convolutional teacher, making Volt competitive with state-of-the-art methods. We then scale supervision through joint training on multiple datasets and show that Volt benefits more from increased scale than domain-specific 3D backbones, achieving state-of-the-art results on several indoor and outdoor semantic segmentation benchmarks. Finally, as a drop-in backbone in a standard 3D instance segmentation pipeline, Volt also sets a new state of the art, highlighting its potential as a simple, scalable, and general-purpose backbone for 3D scene understanding.

arXiv

Tora3: Trajectory-Guided Audio-Video Generation with Physical Coherence

Junchao Liao, Zhenghao Zhang, Xiangyu Meng, Litao Li, Ziying Zhang, Siyu Zhu, Long Qin, Weizhi Wang

Audio-video (AV) generation has recently made strong progress in perceptual quality and multimodal coherence, yet generating content with plausible motion-sound relations remains challenging. Existing methods often produce object motions that are visually unstable and sounds that are only loosely aligned with salient motion or contact events, largely because they lack an explicit motion-aware structure shared by video and audio generation. We present Tora3, a trajectory-guided AV generation framework that improves physical coherence by using object trajectories as a shared kinematic prior. Rather than treating trajectories as a video-only control signal, Tora3 uses them to jointly guide visual motion and acoustic events. Specifically, we design a trajectory-aligned motion representation for video, a kinematic-audio alignment module driven by trajectory-derived second-order kinematic states, and a hybrid flow matching scheme that preserves trajectory fidelity in trajectory-conditioned regions while maintaining local coherence elsewhere. We further curate PAV, a large-scale AV dataset emphasizing motion-relevant patterns with automatically extracted motion annotations. Extensive experiments show that Tora3 improves motion realism, motion-sound synchronization, and overall AV generation quality over strong open-source baselines. Project page: https://ali-videoai.github.io/tora3_page.

arXiv

RePL: Pseudo-label Refinement for Semi-supervised LiDAR Semantic Segmentation

Donghyeon Kwon, Taegyu Park, Suha Kwak

Semi-supervised learning for LiDAR semantic segmentation often suffers from error propagation and confirmation bias caused by noisy pseudo-labels. To tackle this chronic issue, we introduce RePL, a novel framework that enhances pseudo-label quality by identifying and correcting potential errors in pseudo-labels through masked reconstruction, along with a dedicated training strategy. We also provide a theoretical analysis demonstrating the condition under which the pseudo-label refinement is beneficial, and empirically confirm that the condition is mild and clearly met by RePL. Extensive evaluations on the nuScenes-lidarseg and SemanticKITTI datasets show that RePL improves pseudo-label quality substantially, and in consequence, achieves the state of the art in semi-supervised LiDAR semantic segmentation.

arXiv

NAIMA: Semantics Aware RGB Guided Depth Super-Resolution

Tayyab Nasir, Daochang Liu, Ajmal Mian

Guided depth super-resolution (GDSR) is a multi-modal approach for depth map super-resolution that relies on a low-resolution depth map and a high-resolution RGB image to restore finer structural details. However, the misleading color and texture cues indicating depth discontinuities in RGB images often lead to artifacts and blurred depth boundaries in the generated depth map. Recent methods counter this by drawing priors from large pretrained models, but these priors enter the network as decoded predictions such as relative depth, surface normal, or segmentation maps, coupling restoration quality to the accuracy of the decoded prior and requiring auxiliary objectives. We propose a solution that introduces global contextual semantic priors, generated from pretrained vision transformer token embeddings, injecting them directly into the depth branch. Our Guided Token Attention (GTA) module lets multi-level depth encodings act as queries of cross-attention over semantic tokens drawn from progressively deeper layers of the pretrained visual transformer (ViT), scaled by a zero-initialized gate that admits semantic evidence only to the extent that it reduces reconstruction error. The resulting token-to-depth correspondence is aligned implicitly, through learned attention under a single reconstruction loss, rather than through an explicit alignment or distribution-matching objective. Building on this, we present Neural Attention with Implicit Multi-token Alignment (NAIMA), which, to the best of our knowledge, is the first GDSR framework guided by undecoded semantic tokens. NAIMA remains competitive in-distribution while achieving the strongest cross-dataset generalization.

arXiv

Single-Subject Multi-View MRI Super-Resolution via Implicit Neural Representations

Heejong Kim, Abhishek Thanki, Roel van Herten, Daniel Margolis, Mert R Sabuncu

Clinical MRI frequently acquires anisotropic volumes with high in-plane resolution and low through-plane resolution to reduce acquisition time. Multiple orientations are therefore acquired to provide complementary anatomical information. Conventional integration of these views relies on registration followed by interpolation, which can degrade fine structural details. Recent deep learning-based super-resolution (SR) approaches have demonstrated strong performance in enhancing single-view images. However, their clinical reliability is often limited by the need for large-scale training datasets, resulting in increased dependence on cohort-level priors. Self-supervised strategies offer an alternative by learning directly from the target scans. Prior work either neglects the existence of multi-view information or assumes that in-plane information can supervise through-plane reconstruction under the assumption of pre-alignment between images. However, this assumption is rarely satisfied in clinical settings. In this work, we introduce Single-Subject Implicit Multi-View Super-Resolution for MRI (SIMS-MRI), a framework that operates solely on anisotropic multi-view scans from a single patient without requiring pre- or post-processing. Our method combines a multi-resolution hash-encoded implicit representation with learned inter-view alignment to generate a spatially consistent isotropic reconstruction. We validate the SIMS-MRI pipeline on both simulated brain and clinical prostate MRI datasets. Code will be made publicly available for reproducibility: https://github.com/abhshkt/SIMS-MRI

arXiv

Spatio-Temporal Garment Reconstruction Using Diffusion Mapping via Pattern Coordinates

Yingxuan You, Ren Li, Corentin Dumery, Cong Cao, Hao Li, Pascal Fua

Reconstructing 3D clothed humans from monocular images and videos is a fundamental problem with applications in virtual try-on, avatar creation, and mixed reality. Despite significant progress in human body recovery, accurately reconstructing garment geometry, particularly for loose-fitting clothing, remains an open challenge. We propose a unified framework for high-fidelity 3D garment reconstruction from both single images and video sequences. Our approach combines Implicit Sewing Patterns (ISP) with a generative diffusion model to learn expressive garment shape priors in 2D UV space. Leveraging these priors, we introduce a mapping model that establishes correspondences between image pixels, UV pattern coordinates, and 3D geometry, enabling accurate and detailed garment reconstruction from single images. We further extend this formulation to dynamic reconstruction by introducing a spatio-temporal diffusion scheme with test-time guidance to enforce long-range temporal consistency. We also develop analytic projection-based constraints that preserve image-aligned geometry in visible regions while enforcing coherent completion in occluded areas over time. Although trained exclusively on synthetically simulated cloth data, our method generalizes well to real-world imagery and consistently outperforms existing approaches on both tight- and loose-fitting garments. The reconstructed garments preserve fine geometric detail while exhibiting realistic dynamic motion, supporting downstream applications such as texture editing, garment retargeting, and animation.

arXiv

Segmentation-Guided Homography Estimation for Long-Term Planar Tracking

Jonas Serych, Jiri Matas

Recent state-of-the-art visual trackers produce high quality and long-term-stable segmentation masks. We propose to leverage these strengths for planar object tracking, in which the goal is to estimate a precise 8-degrees-of-freedom homography pose, a geometric representation not estimated by segmentation trackers. We present SAM-H - a planar object tracker that estimates homographies from segmentation mask contours via a training-free pipeline. When SAM-H is applied to masks from SAM 2, it sets a new state-of-the-art performance on the challenging PlanarTrack benchmark by a large margin, +18.4pp on the p@5 metric. We further show that segmentation-based and correspondence-based homography estimation are complementary, and propose WOFTSAM, which out-performs all prior methods on both PlanarTrack and POT-210. We also provide precise re-annotations of PlanarTrack initial poses, enabling more accurate benchmarking in the high-precision p@5 metric. The code and the re-annotations are available at https://github.com/serycjon/WOFTSAM

arXiv

Life-Bench: A Benchmark and Knowledge Graph Framework for Multimodal Personalization Beyond Concept Recognition

Xia Hu, Honglei Zhuang, Brian Potetz, Alireza Fathi, BO HU, Babak Samari, Howard Zhou

As large language models increasingly power personal assistants, users expect them to reason over multimodal life histories, from recognizing people to understanding events to aggregating patterns, yet existing benchmarks primarily target concept-level recognition. We introduce Life-Bench, a fully synthetic, human-verified multimodal benchmark of over 11,800 question-answer pairs across 10 tasks, organized by required evidence scope: concept identification, event understanding, and aggregated reasoning. The benchmark's photo-centric personal histories are distributionally aligned with real user accounts under embedding statistic. The interconnected structure of personal data invites graph-based solutions; we propose LifeGraph, a personal knowledge graph framework providing structured retrieval with on-demand access to source visual evidence, showing particular promise on event and aggregated tasks. Systematic evaluation of four retrieval paradigms on Life-Bench demonstrates that accuracy degrades sharply with evidence scope, falling below 0.40 on aggregated tasks, and that no single paradigm dominates across categories. Performance beyond concept recognition remains modest for all evaluated methods, establishing personalization over multimodal histories as an open challenge and Life-Bench as a testbed for future progress.

arXiv

Implicit Neural Representation-Based Continuous Single Image Super-Resolution: An Empirical Benchmark

Tayyab Nasir, Daochang Liu, Ajmal Mian

Implicit neural representation (INR) has become the standard approach for arbitrary-scale image super-resolution (ASSR). However, no systematic empirical study has examined the effectiveness of existing methods under consistent conditions, nor investigated the effects of different training recipes, such as objective design, optimization strategies, and scaling behavior. A rigorous empirical analysis is essential not only for benchmarking performance and revealing true gains but also for establishing the current state of ASSR, identifying saturation limits, and highlighting promising directions. We fill this gap by training 6 INR-based ASSR methods under 9 controlled training recipes and evaluating them across 7 datasets and 7 image quality assessment (IQA) metrics, presenting aggregated performance results through a unified ranking framework that enables more reliable interpretation of performance comparisons and evaluation claims. Furthermore, we investigate the impact of carefully controlled training configurations on perceptual image quality and analyze the role of auxiliary objectives in preserving edges, textures, and fine details during training. Our analysis yields the following insights, previously overlooked: (1) Recent, more complex INR methods provide only marginal improvements over earlier methods, indicating architectural saturation on existing benchmarks. (2) Model performance is strongly correlated with training configurations, a factor neglected in prior comparisons. (3) Auxiliary objectives consistently enhance texture fidelity across architectures compared to standard L1-Loss, emphasizing the role of objective design for targeted perceptual gains. (4) INR-based ASSR exhibits consistent, monotonic scaling behavior across model capacity, training compute, and data diversity, though with diminishing returns as complexity grows.

arXiv

Scientific Image Synthesis: Benchmarking, Methodologies, and Downstream Utility

Honglin Lin, Zheng Liu, Chonghan Qin, Qizhi Pei, Yu Li, Zhanping Zhong, Xin Gao, Yanfeng Wang, Conghui He, Lijun Wu

While synthetic data has proven effective for improving scientific reasoning in the text domain, multimodal reasoning remains constrained by the difficulty of synthesizing scientifically rigorous images. Existing Text-to-Image (T2I) models often produce outputs that are visually plausible yet scientifically incorrect, resulting in a persistent visual-logic divergence that limits their value for downstream reasoning. Motivated by recent advances in next-generation T2I models, we conduct a systematic study of scientific image synthesis across generation paradigms, evaluation, and downstream use. We analyze both direct pixel-based generation and programmatic synthesis, and propose ImgCoder, a logic-driven framework that follows an explicit "understand - plan - code" workflow to improve structural precision. To rigorously assess scientific correctness, we introduce SciGenBench, which evaluates generated images based on information utility and logical validity. Our evaluation reveals systematic failure modes in pixel-based models and highlights a fundamental expressiveness-precision trade-off. Finally, we show that fine-tuning Large Multimodal Models (LMMs) on rigorously verified synthetic scientific images yields consistent reasoning gains, with potential scaling trends analogous to the text domain, validating high-fidelity scientific synthesis as a viable path to unlocking massive multimodal reasoning capabilities.

arXiv

Progressive Masked Refinement Self-supervised Learning for Low-Dose CT Denoising

Yichao Liu, Zongru Shao, Rui Wen, Yueyang Teng, Junwen Guo

Self-supervised learning has been increasingly investigated for low-dose computed tomography (LDCT) image denoising, as it alleviates the dependence on paired normal-dose CT (NDCT) data, which are often difficult to obtain. However, many existing self-supervised blind-spot denoising methods may under-utilize pixel-wise supervisory information loss due to evaluating the training loss only at masked locations. To mitigate this issue, we propose a novel Progressive Masked Refinement Learning framework that progressively refines denoising results while preserving and exploiting available LDCT information. Specifically, we explicitly inject a combination of controlled Gaussian and Poisson noise during training to regularize the denoising process and mitigate trivial identity mapping. Furthermore, we introduce a step-wise mask denoising mechanism that gradually reduces the discrepancy between synthetic corruption and the noise characteristics of LDCT images, enabling more fine-grained learning for denoising. Extensive experiments on the Mayo LDCT dataset demonstrate that the proposed method outperforms existing self-supervised approaches and achieves performance comparable to, or better than, several representative supervised denoising methods.

arXiv

Seeing vs. Believing: Evaluating the Language Bias of Open-Source MLLMs in Counter-Intuitive Scenes

Chen Ling, Tongwei Zhang, Hanqian Li, Nai Ding

Multimodal Large Language Models (MLLMs) have demonstrated remarkable performance in mainstream visual understanding tasks, but their ability to process action scenes that contradict everyday common sense remains undertested. To address this gap, we introduce CAIT, a benchmark comprising 400 high-fidelity synthetic scenes focused on counter-intuitive visual actions, such as ``a rabbit is chasing a tiger'', where visual evidence explicitly contradicts common-sense expectations. We evaluate human, leading proprietary models (e.g., Claude and Gemini), and 14 representative open-source MLLMs. Humans achieve near-perfect performance (around 0.95 accuracy) and proprietary models demonstrate robust understanding (achieving up to 0.88 accuracy), standard open-source instruction-tuned models perform at the chance level. Further analysis demonstrates that this failure is driven by a strong language prior: rather than trusting the visual input, they automatically override the anomalous visual signals with statistically common text descriptions. Although introducing Chain-of-Thought reasoning mechanisms can improve accuracy, it significantly slows down the response and generates a new failure mode: models overthink the scenario and refuse to accept the actual visual content simply because it violates real-world physical laws. Finally, we demonstrate that targeted fine-tuning and structured prompting can effectively mitigate this reliance on language priors, enabling open-source models to accurately ground their reasoning in actual visual evidence.

arXiv

ASemConsist: Adaptive Semantic Feature Control for Training-Free Identity-Consistent Generation

Shin seong Kim, Minjung Shin, Hyunin Cho, Youngjung Uh

Recent text-to-image diffusion models have significantly improved visual quality and text alignment. However, generating a sequence of images while preserving consistent character identity across diverse scenes remains challenging. Existing methods often face a trade-off between maintaining identity consistency and per-image prompt alignment. In this paper, we introduce AsemConsist, a framework that resolves this trade-off through selective text embedding modification, enabling consistent identity preservation without degrading per-image prompt alignment. We further analyze the semantic structure of padding embeddings and find that, in multi-encoder backbones, only padding embeddings that retain prompt-related semantics can effectively serve as semantic containers. Based on this observation, we selectively inject per-image semantics into such padding embeddings while suppressing prompt-irrelevant components. Additionally, we propose an adaptive feature-sharing strategy that automatically evaluates identity specificity and selectively applies constraints only to ambiguous identity prompts. Finally, we propose a unified evaluation metric called SeeSaw, which measures the balance between identity consistency and per-image alignment while evaluating whether identity and per-image prompts are equally reflected in generated images. Our method demonstrates superior performance over existing competitors when built upon SD3.5 and FLUX backbones, highlighting its effectiveness across different architectures and text encoders. Project page: https://minjung-s.github.io/asemconsist

arXiv

Robust Motion Generation using Part-level Reliable Data from Videos

Boyuan Li, Sipeng Zheng, Bin Cao, Ruihua Song, Zongqing Lu

Extracting human motion from large-scale web videos offers a scalable solution to the data scarcity issue in character animation. However, some human parts in many video frames cannot be seen due to off-screen captures or occlusions. It brings a dilemma: discarding the data missing any part limits scale and diversity, while retaining it compromises data quality and model performance. To address this problem, we propose leveraging credible part-level data extracted from videos to enhance motion generation via a robust part-aware masked autoregression model. First, we decompose a human body into five parts and detect the parts clearly seen in a video frame as "credible". Second, the credible parts are encoded into latent tokens by our proposed part-aware variational autoencoder. Third, we propose a robust part-level masked generation model to predict masked credible parts, while ignoring those noisy parts. In addition, we contribute K700-M, a challenging new benchmark comprising approximately 200k real-world motion sequences, for evaluation. Experimental results indicate that our method successfully outperforms baselines on both clean and noisy datasets in terms of motion quality, semantic consistency and diversity. Project page: https://boyuaner.github.io/ropar-main/

arXiv

Layer-Aware Video Composition via Split-then-Merge

Ozgur Kara, Yujia Chen, Ming-Hsuan Yang, James M. Rehg, Wen-Sheng Chu, Du Tran

We present Split-then-Merge (StM), a novel framework designed to enhance control in generative video composition and address its data scarcity problem. Unlike conventional methods relying on annotated datasets or handcrafted rules, StM splits a large corpus of unlabeled videos into dynamic foreground and background layers, then self-composes them to learn how dynamic subjects interact with diverse scenes. This process enables the model to learn the complex compositional dynamics required for realistic video generation. StM introduces a novel transformation-aware training pipeline that utilizes a multi-layer fusion and augmentation to achieve affordance-aware composition, alongside an identity-preservation loss that maintains foreground fidelity during blending. Experiments show StM outperforms SoTA methods in both quantitative benchmarks and in humans/VLM-based qualitative evaluations. More details are available at our project page: https://split-then-merge.github.io

arXiv

C3VDReg: A Benchmark for Local-to-Local Colonoscopic Registration toward Anatomical Localization

Linzhe Jiang, Jiayuan Huang, Sophia Bano, Matthew J. Clarkson, Zhehua Mao, Mobarak I. Hoque

Anatomy-aware colonoscopic navigation requires localizing partial endoscopic observations on a stable 3D reference to support coverage assessment, revisited-region awareness, and CT-guided navigation. However, rigid point cloud registration in the colon differs fundamentally from standard benchmarks: surfaces are locally homogeneous, haustral folds are repetitive, views are highly partial, and reconstructed depth is noisy. We present C3VDReg, a dataset and benchmark derived from the Colonoscopy 3D Video Dataset (C3VD). For each frame, C3VDReg generates source point clouds via depth reprojection and target point clouds by raycasting CT meshes from matched camera poses. The benchmark comprises 10,015 viewpoint-matched partial-to-partial point cloud pairs (including 2,088 held-out test pairs) and evaluates baseline models under a standardized protocol: 8,192 points per cloud, source-only perturbations, fixed pose conventions, and unified metrics. Crucially, C3VDReg enables a systematic investigation of failure modes. We find that high geometric overlap alone is insufficient for reliable pose recovery: despite 74.3-93.1% ground-truth overlap, registration recall remains low across all evaluated methods. Through overlap, pose error, and translation decomposition analyses, we identify translation ambiguity along repetitive tubular anatomy as the primary bottleneck. This challenges the common assumption that increasing overlap or correspondence quality guarantees accurate registration, highlighting the need for stronger anatomical and contextual constraints. Code, model checkpoints, and data are available at https://github.com/linzhe001/C3VDReg .

arXiv

Voronoi-Assisted Optimization for Diffusing Unsigned Distance Fields from Unoriented Points

Jiayi Kong, Chen Zong, Junkai Deng, Xuhui Chen, Fei Hou, Shiqing Xin, Junhui Hou, Chen Qian, Ying He

Unsigned Distance Fields (UDFs) provide a flexible representation for 3D shapes with arbitrary topology, including open and closed surfaces, orientable and non-orientable geometries, and non-manifold structures. While recent neural approaches have shown promise in learning UDFs, they often suffer from numerical instability, high computational cost, and limited controllability. We present a lightweight, network-free method, Voronoi-Assisted Optimization for Diffusing (VAD), to compute UDFs directly from unoriented point clouds. Our approach begins by assigning bi-directional normals to input points, guided by two Voronoi-based geometric criteria encoded in an energy function for optimal alignment. The aligned normals are then diffused to form an approximate UDF gradient field, which is subsequently integrated to recover the final UDF. Experiments demonstrate that VAD robustly handles watertight and open surfaces, as well as complex non-manifold and non-orientable geometries, while remaining computationally efficient and stable.

arXiv

STA-Net: A Decoupled Shape and Texture Attention Network for Lightweight Plant Disease Classification

Zongsen Qiu, Jianjun Wang, Yue Zhou, Zibo Zhou, Rui Chen

Responding to rising global food security needs, precision agriculture and deep learning-based plant disease diagnosis have become crucial. Yet, deploying high-precision models on edge devices is challenging. Most lightweight networks use attention mechanisms designed for generic object recognition, which poorly capture subtle pathological features like irregular lesion shapes and complex textures. To overcome this, we propose a twofold solution: first, using a training-free neural architecture search method (DeepMAD) to create an efficient network backbone for edge devices; second, introducing the Shape-Texture Attention Module (STAM). STAM splits attention into two branches -- one using deformable convolutions (DCNv4) for shape awareness and the other using a Gabor filter bank for texture awareness. On the public CCMT plant disease dataset, our STA-Net model (with 401K parameters and 51.1M FLOPs) reached 89.00% accuracy and an F1 score of 88.96%. Ablation studies confirm STAM significantly improves performance over baseline and standard attention models. Integrating domain knowledge via decoupled attention thus presents a promising path for edge-deployed precision agriculture AI. The source code is available at https://github.com/RzMY/STA-Net.

arXiv

Iwin Transformer: Hierarchical Vision Transformer using Interleaved Windows

Simin Huo, Ning Li

Vision Transformers (ViTs) face two limitations: the rigid resolution dependency of positional embeddings, which complicates cross-resolution fine-tuning, and the quadratic complexity of attention. While Swin Transformer alleviates the latter through window attention, it suffers from fine-tuning. Following the philosophy "no token is an island," we present Iwin Transformer, a position-embedding-free hierarchical vision transformer that couples interleaved window attention with depthwise convolution inside a single block. Attention captures long-range dependencies, while convolution links local neighbors and implicitly encodes spatial position. This design not only reduces the quadratic complexity of attention but also enables two types of scalability: fine-tuning from low to high resolution and weight transfer from 2D to 3D. With window-size adjustment alone, direct $224^2{\rightarrow}384^2$ fine-tuning lifts Iwin-L from 86.4\% to 87.4\% top-1 accuracy on ImageNet-1K. Transferring an ImageNet-pretrained Iwin-T to video achieves 79.1\% on Kinetics-400, outperforming Swin-T (78.8\%) with 15.9\% fewer FLOPs. Iwin also remains competitive on ADE20K segmentation and class-conditional image generation (FlashDiT). Overall, Iwin offers an effective approach to simultaneously tackling the complexity and scalability challenges in ViTs. Code and models are at https://github.com/cominder/Iwin-Transformer.

arXiv

crossMoDA Challenge: Evolution of Cross-Modality Domain Adaptation Techniques for Vestibular Schwannoma and Cochlea Segmentation from 2021 to 2023

Navodini Wijethilake, Reuben Dorent, Marina Ivory, Aaron Kujawa, Stefan Cornelissen, Patrick Langenhuizen, Mohamed Okasha, Anna Oviedova, Hexin Dong, Bogyeong Kang, Guillaume Sallé, Luyi Han, Ziyuan ZHAO, Han Liu, Yubo Fan, Tao Yang, Shahad Hardan, Hussain Alasmawi, Santosh Sanjeev, Yuzhou Zhuang, Satoshi Kondo, Maria Baldeon Calisto, Shaikh Muhammad Uzair Noman, Cancan Chen, Ipek Oguz, Rongguo Zhang, Mina Rezaei, Susana K. Lai-Yuen, Satoshi Kasai, Yunzhi Huang, Chih-Cheng Hung, Mohammad Yaqub, Lisheng Wang, Benoit M. Dawant, Cuntai Guan, Ritse Mann, Vincent Jaouen, Tae-Eui Kam, Li Zhang, Jonathan Shapey, Tom Vercauteren

The cross-Modality Domain Adaptation (crossMoDA) challenge series, initiated in 2021 in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), focuses on unsupervised cross-modality segmentation, learning from contrast-enhanced T1 (ceT1) and transferring to T2 MRI. The task is an extreme example of domain shift chosen to serve as a meaningful and illustrative benchmark. From a clinical application perspective, it aims to automate Vestibular Schwannoma (VS) and cochlea segmentation on T2 scans for more cost-effective VS management. Over time, the challenge objectives have evolved to enhance its clinical relevance. The challenge evolved from using single-institutional data and basic segmentation in 2021 to incorporating multi-institutional data and Koos grading in 2022, and by 2023, it included heterogeneous routine data and sub-segmentation of intra- and extra-meatal tumour components. In this work, we report the findings of the 2022 and 2023 editions and perform a retrospective analysis of the challenge progression over the years. The observations from the successive challenge contributions indicate that the number of outliers decreases with an expanding dataset. This is notable since the diversity of scanning protocols of the datasets concurrently increased. The winning approach of the 2023 edition reduced the number of outliers on the 2021 and 2022 testing data, demonstrating how increased data heterogeneity can enhance segmentation performance even on homogeneous data. However, the cochlea Dice score declined in 2023, likely due to the added complexity from tumour sub-annotations affecting overall segmentation performance. While progress is still needed for clinically acceptable VS segmentation, the plateauing performance suggests that a more challenging cross-modal task may better serve future benchmarking.

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