Airway Labeling Meets Clinical Applications: Reflecting Topology Consistency and Outliers via Learnable Attentions

Accurate airway anatomical labeling is crucial for clinicians to identify and navigate complex bronchial structures during bronchoscopy. Automatic airway anatomical labeling is challenging due to significant individual variability and anatomical variations. Previous methods are prone to generate inconsistent predictions, which is harmful for preoperative planning and intraoperative navigation. This paper aims to address these challenges by proposing a novel method that enhances topological consistency and improves the detection of abnormal airway branches. We propose a novel approach incorporating two modules: the Soft Subtree Consistency (SSC) and the Abnormal Branch Saliency (ABS). The SSC module constructs a soft subtree to capture clinically relevant topological relationships, allowing for flexible feature aggregation within and across subtrees. The ABS module facilitates the interaction between node features and prototypes to distinguish abnormal branches, preventing the erroneous aggregation of features between normal and abnormal nodes. Evaluated on a challenging dataset characterized by severe airway distortion and atrophy, our method achieves superior performance compared to state-of-the-art approaches. Specifically, it attains a 91.4% accuracy at the segmental level and an 83.7% accuracy at the subsegmental level, representing a 1.4% increase in subsegmental accuracy and a 3.1% increase in topological consistency. Notably, the method demonstrates reliable performance in cases with disease-induced airway deformities, ensuring consistent and accurate labeling.

From Real Artifacts to Virtual Reference: A Robust Framework for Translating Endoscopic Images

Domain adaptation, which bridges the distributions across different modalities, plays a crucial role in multimodal medical image analysis. In endoscopic imaging, combining pre-operative data with intra-operative imaging is important for surgical planning and navigation. However, existing domain adaptation methods are hampered by distribution shift caused by in vivo artifacts, necessitating robust techniques for aligning noisy and artifact abundant patient endoscopic videos with clean virtual images reconstructed from pre-operative tomographic data for pose estimation during intraoperative guidance. This paper presents an artifact-resilient image translation method and an associated benchmark for this purpose. The method incorporates a novel ``local-global'' translation framework and a noise-resilient feature extraction strategy. For the former, it decouples the image translation process into a local step for feature denoising, and a global step for global style transfer. For feature extraction, a new contrastive learning strategy is proposed, which can extract noise-resilient features for establishing robust correspondence across domains. Detailed validation on both public and in-house clinical datasets has been conducted, demonstrating significantly improved performance compared to the current state-of-the-art.

Topology-Aware Exploration of Circle of Willis for CTA and MRA: Segmentation, Detection, and Classification

The Circle of Willis (CoW) vessels is critical to connecting major circulations of the brain. The topology of the vascular structure is clinical significance to evaluate the risk, severity of the neuro-vascular diseases. The CoW has two representative angiographic imaging modalities, computed tomography angiography (CTA) and magnetic resonance angiography (MRA). TopCow24 provided 125 paired CTA-MRA dataset for the analysis of CoW. To explore both CTA and MRA images in a unified framework to learn the inherent topology of Cow, we construct the universal dataset via independent intensity preprocess, followed by joint resampling and normarlization. Then, we utilize the topology-aware loss to enhance the topology completeness of the CoW and the discrimination between different classes. A complementary topology-aware refinement is further conducted to enhance the connectivity within the same class. Our method was evaluated on all the three tasks and two modalities, achieving competitive results. In the final test phase of TopCow24 Challenge, we achieved the second place in the CTA-Seg-Task, the third palce in the CTA-Box-Task, the first place in the CTA-Edg-Task, the second place in the MRA-Seg-Task, the third palce in the MRA-Box-Task, the second place in the MRA-Edg-Task.

PASS:Test-Time Prompting to Adapt Styles and Semantic Shapes in Medical Image Segmentation

Test-time adaptation (TTA) has emerged as a promising paradigm to handle the domain shifts at test time for medical images from different institutions without using extra training data. However, existing TTA solutions for segmentation tasks suffer from (1) dependency on modifying the source training stage and access to source priors or (2) lack of emphasis on shape-related semantic knowledge that is crucial for segmentation tasks.Recent research on visual prompt learning achieves source-relaxed adaptation by extended parameter space but still neglects the full utilization of semantic features, thus motivating our work on knowledge-enriched deep prompt learning. Beyond the general concern of image style shifts, we reveal that shape variability is another crucial factor causing the performance drop. To address this issue, we propose a TTA framework called PASS (Prompting to Adapt Styles and Semantic shapes), which jointly learns two types of prompts: the input-space prompt to reformulate the style of the test image to fit into the pretrained model and the semantic-aware prompts to bridge high-level shape discrepancy across domains. Instead of naively imposing a fixed prompt, we introduce an input decorator to generate the self-regulating visual prompt conditioned on the input data. To retrieve the knowledge representations and customize target-specific shape prompts for each test sample, we propose a cross-attention prompt modulator, which performs interaction between target representations and an enriched shape prompt bank. Extensive experiments demonstrate the superior performance of PASS over state-of-the-art methods on multiple medical image segmentation datasets. The code is available at https://github.com/EndoluminalSurgicalVision-IMR/PASS.

Leveraging Open Knowledge for Advancing Task Expertise in Large Language Models

The cultivation of expertise for large language models (LLMs) to solve tasks of specific areas often requires special-purpose tuning with calibrated behaviors on the expected stable outputs. To avoid huge cost brought by manual preparation of instruction datasets and training resources up to hundreds of hours, the exploitation of open knowledge including a wealth of low rank adaptation (LoRA) models and instruction datasets serves as a good starting point. However, existing methods on model and data selection focus on the performance of general-purpose capabilities while neglecting the knowledge gap exposed in domain-specific deployment. In the present study, we propose to bridge such gap by introducing few human-annotated samples (i.e., K-shot) for advancing task expertise of LLMs with open knowledge. Specifically, we develop an efficient and scalable pipeline to cost-efficiently produce task experts where K-shot data intervene in selecting the most promising expert candidates and the task-relevant instructions. A mixture-of-expert (MoE) system is built to make the best use of individual-yet-complementary knowledge between multiple experts. We unveil the two keys to the success of a MoE system, 1) the abidance by K-shot, and 2) the insistence on diversity. For the former, we ensure that models that truly possess problem-solving abilities on K-shot are selected rather than those blind guessers. Besides, during data selection, instructions that share task-relevant contexts with K-shot are prioritized. For the latter, we highlight the diversity of constituting experts and that of the fine-tuning instructions throughout the model and data selection process. Extensive experimental results confirm the superiority of our approach over existing methods on utilization of open knowledge across various tasks. Codes and models will be released later.

LNQ 2023 challenge: Benchmark of weakly-supervised techniques for mediastinal lymph node quantification

Accurate assessment of lymph node size in 3D CT scans is crucial for cancer staging, therapeutic management, and monitoring treatment response. Existing state-of-the-art segmentation frameworks in medical imaging often rely on fully annotated datasets. However, for lymph node segmentation, these datasets are typically small due to the extensive time and expertise required to annotate the numerous lymph nodes in 3D CT scans. Weakly-supervised learning, which leverages incomplete or noisy annotations, has recently gained interest in the medical imaging community as a potential solution. Despite the variety of weakly-supervised techniques proposed, most have been validated only on private datasets or small publicly available datasets. To address this limitation, the Mediastinal Lymph Node Quantification (LNQ) challenge was organized in conjunction with the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023). This challenge aimed to advance weakly-supervised segmentation methods by providing a new, partially annotated dataset and a robust evaluation framework. A total of 16 teams from 5 countries submitted predictions to the validation leaderboard, and 6 teams from 3 countries participated in the evaluation phase. The results highlighted both the potential and the current limitations of weakly-supervised approaches. On one hand, weakly-supervised approaches obtained relatively good performance with a median Dice score of $61.0\%$. On the other hand, top-ranked teams, with a median Dice score exceeding $70\%$, boosted their performance by leveraging smaller but fully annotated datasets to combine weak supervision and full supervision. This highlights both the promise of weakly-supervised methods and the ongoing need for high-quality, fully annotated data to achieve higher segmentation performance.

Unleashing the Power of Data Tsunami: A Comprehensive Survey on Data Assessment and Selection for Instruction Tuning of Language Models

Instruction tuning plays a critical role in aligning large language models (LLMs) with human preference. Despite the vast amount of open instruction datasets, naively training a LLM on all existing instructions may not be optimal and practical. To pinpoint the most beneficial datapoints, data assessment and selection methods have been proposed in the fields of natural language processing (NLP) and deep learning. However, under the context of instruction tuning, there still exists a gap in knowledge on what kind of data evaluation metrics can be employed and how they can be integrated into the selection mechanism. To bridge this gap, we present a comprehensive review on existing literature of data assessment and selection especially for instruction tuning of LLMs. We systematically categorize all applicable methods into quality-based, diversity-based, and importance-based ones where a unified, fine-grained taxonomy is structured. For each category, representative methods are elaborated to describe the landscape of relevant research. In addition, comparison between latest methods is conducted on their officially reported results to provide in-depth discussions on their limitations. Finally, we summarize the open challenges and propose the promosing avenues for future studies. All related contents are available at https://github.com/yuleiqin/fantastic-data-engineering.

SLoRD: Structural Low-Rank Descriptors for Shape Consistency in Vertebrae Segmentation

Automatic and precise segmentation of vertebrae from CT images is crucial for various clinical applications. However, due to a lack of explicit and strict constraints, existing methods especially for single-stage methods, still suffer from the challenge of intra-vertebrae segmentation inconsistency, which refers to multiple label predictions inside a singular vertebra. For multi-stage methods, vertebrae detection serving as the first step, is affected by the pathology and mental implants. Thus, incorrect detections cause biased patches before segmentation, then lead to inconsistent labeling and segmentation. In our work, motivated by the perspective of instance segmentation, we try to label individual and complete binary masks to address this limitation. Specifically, a contour-based network is proposed based on Structural Low-Rank Descriptors for shape consistency, termed SLoRD. These contour descriptors are acquired in a data-driven manner in advance. For a more precise representation of contour descriptors, we adopt the spherical coordinate system and devise the spherical centroid. Besides, the contour loss is designed to impose explicit consistency constraints, facilitating regressed contour points close to vertebral boundaries. Quantitative and qualitative evaluations on VerSe 2019 demonstrate the superior performance of our framework over other single-stage and multi-stage state-of-the-art (SOTA) methods.

Dynamic Position Transformation and Boundary Refinement Network for Left Atrial Segmentation

Left atrial (LA) segmentation is a crucial technique for irregular heartbeat (i.e., atrial fibrillation) diagnosis. Most current methods for LA segmentation strictly assume that the input data is acquired using object-oriented center cropping, while this assumption may not always hold in practice due to the high cost of manual object annotation. Random cropping is a straightforward data pre-processing approach. However, it 1) introduces significant irregularities and incompleteness in the input data and 2) disrupts the coherence and continuity of object boundary regions. To tackle these issues, we propose a novel Dynamic Position transformation and Boundary refinement Network (DPBNet). The core idea is to dynamically adjust the relative position of irregular targets to construct their contextual relationships and prioritize difficult boundary pixels to enhance foreground-background distinction. Specifically, we design a shuffle-then-reorder attention module to adjust the position of disrupted objects in the latent space using dynamic generation ratios, such that the vital dependencies among these random cropping targets could be well captured and preserved. Moreover, to improve the accuracy of boundary localization, we introduce a dual fine-grained boundary loss with scenario-adaptive weights to handle the ambiguity of the dual boundary at a fine-grained level, promoting the clarity and continuity of the obtained results. Extensive experimental results on benchmark dataset have demonstrated that DPBNet consistently outperforms existing state-of-the-art methods.

Efficient Domain Adaptation for Endoscopic Visual Odometry

Visual odometry plays a crucial role in endoscopic imaging, yet the scarcity of realistic images with ground truth poses poses a significant challenge. Therefore, domain adaptation offers a promising approach to bridge the pre-operative planning domain with the intra-operative real domain for learning odometry information. However, existing methodologies suffer from inefficiencies in the training time. In this work, an efficient neural style transfer framework for endoscopic visual odometry is proposed, which compresses the time from pre-operative planning to testing phase to less than five minutes. For efficient traing, this work focuses on training modules with only a limited number of real images and we exploit pre-operative prior information to dramatically reduce training duration. Moreover, during the testing phase, we propose a novel Test Time Adaptation (TTA) method to mitigate the gap in lighting conditions between training and testing datasets. Experimental evaluations conducted on two public endoscope datasets showcase that our method achieves state-of-the-art accuracy in visual odometry tasks while boasting the fastest training speeds. These results demonstrate significant promise for intra-operative surgery applications.