Multi-Modality Abdominal Multi-Organ Segmentation with Deep Supervised 3D Segmentation Model

• URL: http://arxiv.org/abs/2208.12041v1
• Date: Wed, 24 Aug 2022 03:37:54 GMT
• Title: Multi-Modality Abdominal Multi-Organ Segmentation with Deep Supervised 3D Segmentation Model
• Authors: Satoshi Kondo, Satoshi Kasai
• Abstract summary: We present our solution for the AMOS 2022 challenge. We employ residual U-Net with deep super vision as our base model. The experimental results show that the mean scores of Dice similarity coefficient and normalized surface dice are 0.8504 and 0.8476 for CT only task and CT/MRI task, respectively.
• Score: 0.12183405753834559
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: To promote the development of medical image segmentation technology, AMOS, a large-scale abdominal multi-organ dataset for versatile medical image segmentation, is provided and AMOS 2022 challenge is held by using the dataset. In this report, we present our solution for the AMOS 2022 challenge. We employ residual U-Net with deep super vision as our base model. The experimental results show that the mean scores of Dice similarity coefficient and normalized surface dice are 0.8504 and 0.8476 for CT only task and CT/MRI task, respectively.

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