Automated Lesion Segmentation in Whole-Body Multi-Tracer PET-CT Imaging: Contributions to the AutoPET 2024 Challenge

Основные понятия

This study proposes a workflow for automated segmentation of pathological lesions in whole-body PET-CT images, contributing to the AutoPET 2024 challenge. The approach involves image preprocessing, tracer classification, and lesion segmentation using deep learning models.

Аннотация

This study aims to address the challenge of automatic segmentation of pathological regions within whole-body PET-CT volumes, which has the potential to streamline various clinical applications such as diagnosis, prognosis, and treatment planning.
The authors propose a workflow that incorporates the following key steps:

Preprocessing:

Cropping of CT volumes to preserve anatomical structures and minimize background inclusion
Intensity normalization of CT images by clipping values within a predefined range and applying channel-wise Z-score normalization

Multitracer Segmentation:

Utilization of two deep learning models - SegResNet and nnU-Net ResENCL
The nnU-Net ResENCL model consistently outperformed the SegResNet model, achieving an average Dice score of 0.548 across 1611 training subjects, 0.631 and 0.559 for classified FDG and PSMA subjects, respectively, and 0.792 on the preliminary testing phase dataset.

Lesion Tracer Segmentation:

Proposed an alternative pipeline that first identifies the PET radiotracer (FDG or PSMA) and then trains separate segmentation networks for each tracer type
The high robustness of the prior classification model (ROC-AUC ~0.99) highlights the potential benefits of this approach, but it was not thoroughly evaluated due to time constraints.

The authors discuss the limitations of their approach, such as instances of over- and under-segmentation, and the presence of false positive detections due to healthy structures exhibiting abnormal tracer uptake. Potential strategies to address these issues, such as incorporating anatomical structures as prior information and applying rule-based post-processing, are identified as future research directions.

Статистика

"The utilized training dataset comprises 1611 multi-institutional co-registered PET-CT volumes, with 1014 subjects from the FDG cohort and 597 from the PSMA cohort."
"In the FDG cohort, 513 subjects served as negative controls, while the remaining 501 had histologically confirmed diagnoses of malignant melanoma, lymphoma, or lung cancer."
"The PSMA cohort included pre-and/or post-therapeutic PET-CT volumes from 537 male subjects diagnosed with prostate carcinoma and 60 subjects without PSMA-avid tumor lesions."

Цитаты

"The automatic segmentation of pathological regions within whole-body PET-CT volumes has the potential to streamline various clinical applications such as diagnosis, prognosis, and treatment planning."
"Deep learning (DL) techniques have significantly advanced automatic segmentation of both anatomical structures and pathological regions in structural imaging modalities like CT and MRI over the past decade."
"The AutoPET challenge [6], introduced at MICCAI in 2022 and reiterated in 2023, focused on evaluating the performance of automated lesion segmentation methods in whole-body FDG-PET-CT scans."

Ключевые выводы из

Lesion Segmentation in Whole-Body Multi-Tracer PET-CT Images; a Contribution to AutoPET 2024 Challenge

by Mehdi Astara... в arxiv.org 09-24-2024

https://arxiv.org/pdf/2409.14475.pdf

Дополнительные вопросы

How can the proposed workflow be further improved to reduce the instances of over- and under-segmentation, as well as false positive detections?

To enhance the proposed workflow and mitigate over- and under-segmentation, as well as false positive detections, several strategies can be implemented.

Post-Processing Techniques: Implementing rule-based post-processing algorithms can help refine the segmentation masks. Techniques such as morphological operations (e.g., erosion and dilation) can be used to smooth the boundaries of the segmented lesions, reducing noise and improving the delineation of tumor boundaries.

Incorporation of Anatomical Priors: Utilizing anatomical information from the CT scans can significantly improve segmentation accuracy. By integrating prior knowledge of normal anatomical structures, the model can be guided to avoid false positives in regions where healthy tissues exhibit abnormal tracer uptake.

Multi-Scale Feature Extraction: Enhancing the model architecture to include multi-scale feature extraction can help capture lesions of varying sizes more effectively. This can be achieved by incorporating dilated convolutions or pyramid pooling modules that allow the model to learn features at different resolutions.

Ensemble Learning: Combining predictions from multiple models can lead to more robust segmentation results. An ensemble approach can average out the predictions, reducing the impact of individual model biases and improving overall accuracy.

Adaptive Thresholding: Instead of using a fixed threshold for segmentation, adaptive thresholding techniques can be employed to dynamically adjust the threshold based on local image characteristics, which can help in accurately delineating lesions in varying backgrounds.

Increased Training Data Diversity: Expanding the training dataset to include a wider variety of cases, including those with atypical presentations, can help the model generalize better and reduce the likelihood of overfitting to specific patterns.

What additional preprocessing techniques or architectural modifications could be explored to enhance the generalization capabilities of the segmentation models across different PET tracers and clinical settings?

To improve the generalization capabilities of segmentation models across various PET tracers and clinical settings, the following preprocessing techniques and architectural modifications can be explored:

Data Augmentation: Implementing extensive data augmentation techniques, such as rotation, scaling, flipping, and elastic deformations, can help create a more diverse training dataset. This diversity can enhance the model's ability to generalize across different imaging conditions and patient populations.

Domain Adaptation Techniques: Utilizing domain adaptation methods can help the model learn to generalize from one tracer to another. Techniques such as adversarial training can be employed to minimize the domain shift between different PET tracers, allowing the model to perform well across various clinical settings.

Transfer Learning: Leveraging pre-trained models on large datasets can provide a strong initialization for the segmentation task. Fine-tuning these models on the specific PET-CT dataset can lead to improved performance, especially when the available training data is limited.

Attention Mechanisms: Incorporating attention mechanisms into the model architecture can help the network focus on relevant features while ignoring irrelevant background noise. This can be particularly beneficial in complex images where lesions may be obscured by surrounding tissues.

Hybrid Model Architectures: Exploring hybrid architectures that combine convolutional neural networks (CNNs) with recurrent neural networks (RNNs) or transformers can enhance the model's ability to capture spatial and temporal dependencies in the data, improving segmentation accuracy.

Normalization Techniques: Implementing advanced normalization techniques, such as instance normalization or batch normalization, can help standardize the input data and improve the model's robustness to variations in intensity and contrast across different PET scans.

What potential clinical applications and decision-support systems could benefit from the accurate and efficient lesion segmentation in whole-body PET-CT scans, and how might this technology impact patient care and outcomes?

Accurate and efficient lesion segmentation in whole-body PET-CT scans has several potential clinical applications and can significantly enhance decision-support systems, ultimately impacting patient care and outcomes in the following ways:

Personalized Treatment Planning: By providing precise delineation of tumor boundaries, lesion segmentation can facilitate personalized treatment planning, allowing oncologists to tailor therapies based on the specific characteristics and extent of the disease. This can lead to more effective treatment regimens and improved patient outcomes.

Monitoring Treatment Response: Automated segmentation can enable consistent and objective assessment of tumor response to therapy over time. By quantifying changes in tumor volume and metabolic activity, clinicians can make informed decisions regarding treatment adjustments, potentially leading to better management of the disease.

Radiotherapy Planning: Accurate segmentation of tumors and surrounding healthy tissues is crucial for effective radiotherapy planning. By ensuring that radiation is precisely targeted to tumor regions while sparing healthy tissues, segmentation can enhance treatment efficacy and reduce side effects.

Clinical Decision Support Systems: Integrating automated segmentation into clinical decision support systems can provide radiologists and oncologists with valuable insights, improving diagnostic accuracy and reducing interpretation time. This can lead to faster clinical decisions and improved patient throughput.

Research and Clinical Trials: Accurate lesion segmentation can facilitate the collection of standardized imaging biomarkers for research and clinical trials. This can enhance the evaluation of new therapies and contribute to the development of evidence-based treatment protocols.

Patient Stratification: By identifying and quantifying tumor heterogeneity through segmentation, clinicians can stratify patients based on their risk profiles. This can lead to more targeted interventions and improved prognostic accuracy.

Enhanced Workflow Efficiency: Automating the segmentation process can significantly reduce the time and effort required for manual analysis, allowing radiologists to focus on more complex cases and improving overall workflow efficiency in clinical settings.

In summary, the integration of accurate lesion segmentation technology into clinical practice has the potential to transform patient care by enabling personalized treatment approaches, improving monitoring and assessment, and enhancing the overall efficiency of healthcare delivery.