NUMSnet: Nested-U Multi-class Segmentation network for 3D Medical Image Stacks

• URL: http://arxiv.org/abs/2304.02713v1
• Date: Wed, 5 Apr 2023 19:16:29 GMT
• Title: NUMSnet: Nested-U Multi-class Segmentation network for 3D Medical Image Stacks
• Authors: Sohini Roychowdhury
• Abstract summary: NUMSnet is a novel variant of the Unet model that transmits pixel neighborhood features across scans through nested layers. We analyze the semantic segmentation performance of the NUMSnet model in comparison with several Unet model variants. The proposed model can standardize multi-class semantic segmentation on a variety of volumetric image stacks with minimal training dataset.
• Score: 1.2335698325757494
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Semantic segmentation for medical 3D image stacks enables accurate volumetric reconstructions, computer-aided diagnostics and follow up treatment planning. In this work, we present a novel variant of the Unet model called the NUMSnet that transmits pixel neighborhood features across scans through nested layers to achieve accurate multi-class semantic segmentations with minimal training data. We analyze the semantic segmentation performance of the NUMSnet model in comparison with several Unet model variants to segment 3-7 regions of interest using only 10% of images for training per Lung-CT and Heart-CT volumetric image stacks. The proposed NUMSnet model achieves up to 20% improvement in segmentation recall with 4-9% improvement in Dice scores for Lung-CT stacks and 2.5-10% improvement in Dice scores for Heart-CT stacks when compared to the Unet++ model. The NUMSnet model needs to be trained by ordered images around the central scan of each volumetric stack. Propagation of image feature information from the 6 nested layers of the Unet++ model are found to have better computation and segmentation performances than propagation of all up-sampling layers in a Unet++ model. The NUMSnet model achieves comparable segmentation performances to existing works, while being trained on as low as 5\% of the training images. Also, transfer learning allows faster convergence of the NUMSnet model for multi-class semantic segmentation from pathology in Lung-CT images to cardiac segmentations in Heart-CT stacks. Thus, the proposed model can standardize multi-class semantic segmentation on a variety of volumetric image stacks with minimal training dataset. This can significantly reduce the cost, time and inter-observer variabilities associated with computer-aided detections and treatment.

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