SegAN: Adversarial Network with Multi-scale $L_1$ Loss for Medical Image Segmentation (1706.01805v2)

Abstract: Inspired by classic generative adversarial networks (GAN), we propose a novel end-to-end adversarial neural network, called SegAN, for the task of medical image segmentation. Since image segmentation requires dense, pixel-level labeling, the single scalar real/fake output of a classic GAN's discriminator may be ineffective in producing stable and sufficient gradient feedback to the networks. Instead, we use a fully convolutional neural network as the segmentor to generate segmentation label maps, and propose a novel adversarial critic network with a multi-scale $L_1$ loss function to force the critic and segmentor to learn both global and local features that capture long- and short-range spatial relationships between pixels. In our SegAN framework, the segmentor and critic networks are trained in an alternating fashion in a min-max game: The critic takes as input a pair of images, (original_image $$ predicted_label_map, original_image $$ ground_truth_label_map), and then is trained by maximizing a multi-scale loss function; The segmentor is trained with only gradients passed along by the critic, with the aim to minimize the multi-scale loss function. We show that such a SegAN framework is more effective and stable for the segmentation task, and it leads to better performance than the state-of-the-art U-net segmentation method. We tested our SegAN method using datasets from the MICCAI BRATS brain tumor segmentation challenge. Extensive experimental results demonstrate the effectiveness of the proposed SegAN with multi-scale loss: on BRATS 2013 SegAN gives performance comparable to the state-of-the-art for whole tumor and tumor core segmentation while achieves better precision and sensitivity for Gd-enhance tumor core segmentation; on BRATS 2015 SegAN achieves better performance than the state-of-the-art in both dice score and precision.

• The paper presents a novel adversarial framework that replaces pixel-wise loss with a multi-scale L1 loss to better capture spatial context in medical images.
• It integrates a fully convolutional encoder-decoder and a feature-based critic to enable end-to-end segmentation without additional post-processing steps.
• Extensive tests on BRATS datasets show that SegAN achieves competitive dice scores and improved precision, highlighting its potential for advanced medical image analysis.

SegAN: Adversarial Network with Multi-scale $L_1$ Loss for Medical Image Segmentation

The paper introduces SegAN, an innovative adversarial network tailored for semantic segmentation of medical images. Unlike classic GANs, which utilize a singular scalar output from the discriminator, SegAN proposes a fully convolutional approach that enhances the learning of global and local features through a novel multi-scale $L_1$ loss function.

Architecture and Methodology

SegAN consists of two primary components: the Segmentor and the Critic. The Segmentor is structured as a fully convolutional encoder-decoder network, capable of generating label maps from input images. In contrast, the Critic network evaluates these outputs by incorporating a multi-scale loss based on hierarchical features extracted at various depths. This strategy avoids the limitations of pixel-wise loss typical in standard CNN architectures.

The feature-based adversarial mechanism allows SegAN to train effectively in a min-max game setting, where the Segmentor minimizes and the Critic maximizes the multi-scale loss. Unlike previous segmentation methods that necessitate post-processing steps such as CRFs, SegAN integrates these dependencies directly into the training process.

Experimental Results

Utilizing datasets from the MICCAI BRATS brain tumor segmentation challenge, SegAN demonstrates superior performance. On BRATS 2013, it shows comparable dice scores for whole tumor and tumor core segmentation, while outperforming in Gd-enhanced tumor core segmentation compared to existing methods like U-net. Similarly, on BRATS 2015, SegAN exceeds state-of-the-art results, particularly in terms of precision and dice score.

Contributions and Implications

SegAN's contribution lies in its departure from traditional GAN frameworks for segmentation, addressing the nuanced requirements of medical image analysis. The integration of multi-scale $L_1$ loss shows the potential for enforcing robust learning of spatial relationships, leading to smoother and more accurate segmentation results.

This advancement has important implications for the future of medical imaging, where precise segmentation is critical. The framework's generalizability also hints at applications beyond medical domains, offering a promising direction for semantic segmentation tasks across various fields.

Future Directions

Future work may explore further optimization of the multi-scale loss function. Additionally, adapting SegAN to leverage 3D convolutions could extend its applicability to volumetric medical imaging modalities. Assessments of SegAN's performance in varied imaging challenges may solidify its standing as a versatile tool in AI-driven image analysis. As adversarial networks continue to evolve, SegAN provides a notable instance of innovation tailored to address the complex requirements of dense pixel-wise labeling tasks.