HeMIS: Hetero-Modal Image Segmentation (1607.05194v1)

Abstract: We introduce a deep learning image segmentation framework that is extremely robust to missing imaging modalities. Instead of attempting to impute or synthesize missing data, the proposed approach learns, for each modality, an embedding of the input image into a single latent vector space for which arithmetic operations (such as taking the mean) are well defined. Points in that space, which are averaged over modalities available at inference time, can then be further processed to yield the desired segmentation. As such, any combinatorial subset of available modalities can be provided as input, without having to learn a combinatorial number of imputation models. Evaluated on two neurological MRI datasets (brain tumors and MS lesions), the approach yields state-of-the-art segmentation results when provided with all modalities; moreover, its performance degrades remarkably gracefully when modalities are removed, significantly more so than alternative mean-filling or other synthesis approaches.

• The paper presents a novel framework that uses learned embeddings and statistical moment fusion to achieve robust segmentation even when some imaging modalities are absent.
• The model processes each modality with independent convolutional pipelines and fuses features to deliver state-of-the-art performance on brain tumor and MS lesion datasets.
• The research highlights the potential for adaptable deep learning applications in clinical settings where restricted modality availability is a constant challenge.

An Expert Analysis of HeMIS: Hetero-Modal Image Segmentation

The paper introduces HeMIS, a deep learning framework tailored for robust segmentation of medical images, particularly in contexts where certain imaging modalities may be absent, a common situation in clinical settings. The significance of this contribution lies in its unique approach to handling missing modalities without attempting imputation or synthesis. Instead, HeMIS leverages learned embeddings to process available data effectively, ensuring optimal segmentation performance across varying combinations of input modalities.

Methodology

HeMIS employs a modular architecture where each modality is processed independently through dedicated convolutional pipelines. The core innovation is the abstraction layer, facilitating the fusion of multiple modality-specific feature maps by calculating their statistical moments, such as mean and variance. These aggregated features are subsequently fed into a secondary set of convolutional layers to produce the segmentation output. The method does not require the presence of all modalities, a critical advancement over traditional approaches that struggle with missing data and typically resort to suboptimal solutions like mean-filling or complex data imputation models.

Experimental Evaluation

The framework's efficacy is demonstrated using two prominent neurological MRI datasets: one for brain tumors and another for Multiple Sclerosis (MS) lesions. Notably, HeMIS achieves superior state-of-the-art results even compared to established methods, maintaining segmentation accuracy competitive with or exceeding existing techniques when all modalities are available. The results indicate a remarkably graceful degradation in performance as modalities are progressively removed. In contrast, conventional methods demonstrate significant drops in accuracy under similar conditions.

Implications and Future Directions

HeMIS underscores the potential for flexible, robust deep learning architectures in medical imaging, paving the way for practical applications where acquisition constraints or cost might limit the availability of complete modality sets. The ability to adapt dynamically to the presence or absence of data types without significant loss of performance is particularly valuable in real-world clinical applications, offering a pathway to implement more adaptable AI systems in healthcare environments.

For future exploration, extending this methodology to different imaging contexts, such as combining MRI with CT or PET data, could broaden its applicability. Additionally, investigating alternative statistical fusion techniques within the abstraction layer might offer further improvements, potentially enhancing the framework's capability to deal with increasingly heterogeneous datasets.

Overall, HeMIS presents a significant step forward in medical image segmentation research, providing both a practical tool for current clinical needs and a promising foundation for ongoing advances in multi-modal data processing.