CNN-based Segmentation of Medical Imaging Data

2017/01/11 by Baris Kayalibay, Grady W. Jensen, Kayalibay, Baris +4 · 23 citations
Computer Science · Engineering · Neuroscience · #AI in cancer detection #Brain Tumor Detection and Classification #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Medical Imaging and Analysis #cs.CV

paper · pdf · doi:10.48550/arxiv.1701.03056

24 pages, Code available on https://github.com/BRML/CNNbasedMedicalSegmentation

openalex publication_date 2017/01/11 · arxiv created 2017/07/25 · arxiv updated 2017/07/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Convolutional neural networks have been applied to a wide variety of computer vision tasks. Recent advances in semantic segmentation have enabled their application to medical image segmentation. While most CNNs use two-dimensional kernels, recent CNN-based publications on medical image segmentation featured three-dimensional kernels, allowing full access to the three-dimensional structure of medical images. Though closely related to semantic segmentation, medical image segmentation includes specific challenges that need to be addressed, such as the scarcity of labelled data, the high class imbalance found in the ground truth and the high memory demand of three-dimensional images. In this work, a CNN-based method with three-dimensional filters is demonstrated and applied to hand and brain MRI. Two modifications to an existing CNN architecture are discussed, along with methods on addressing the aforementioned challenges. While most of the existing literature on medical image segmentation focuses on soft tissue and the major organs, this work is validated on data both from the central nervous system as well as the bones of the hand.

Citations

• The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)
• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Gradient-based learning applied to document recognition
• Deeply-Supervised Nets
• 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
• Visualizing and Understanding Convolutional Networks
• Fully Convolutional Networks for Semantic Segmentation
• Learning Deconvolution Network for Semantic Segmentation

Cited by

• TGC-Net: A Structure-Aware and Semantically-Aligned Framework for Text-Guided Medical Image Segmentation
• DinoAtten3D: Slice-Level Attention Aggregation of DinoV2 for 3D Brain MRI Anomaly Classification
• CU-Net: Cascaded U-Net with Loss Weighted Sampling for Brain Tumor Segmentation
• ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation
• Multiclass Spinal Cord Tumor Segmentation on MRI with Deep Learning
• Leveraging Human Selective Attention for Medical Image Analysis with Limited Training Data
• Deep Learning for Lung Cancer Detection: Tackling the Kaggle Data Science Bowl 2017 Challenge
• Morphological Operation Residual Blocks: Enhancing 3D Morphological Feature Representation in Convolutional Neural Networks for Semantic Segmentation of Medical Images
• Convolutional Recurrent Residual U-Net Embedded with Attention Mechanism and Focal Tversky Loss Function for Cancerous Nuclei Detection
• UAVD-Mamba: Deformable Token Fusion Vision Mamba for Multimodal UAV Detection
• Three-dimensional end-to-end deep learning for brain MRI analysis
• Pixel-level Certified Explanations via Randomized Smoothing
• Self-supervised Lesion Change Detection and Localisation in Longitudinal Multiple Sclerosis Brain Imaging
• Autofocus Layer for Semantic Segmentation
• TK-Mamba: Marrying KAN With Mamba for Text-Driven 3D Medical Image Segmentation
• Responsible AI: Gender bias assessment in emotion recognition
• Breast Cancer Classification in Deep Ultraviolet Fluorescence Images Using a Patch-Level Vision Transformer Framework
• 3D U²-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation
• 3D Consistent & Robust Segmentation of Cardiac Images by Deep Learning with Spatial Propagation
• Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges
• Domain-specific cues improve robustness of deep learning-based segmentation of CT volumes
• Hybrid CNN with Chebyshev Polynomial Expansion for Medical Image Analysis
• Human Activity Recognition using RGB-Event based Sensors: A Multi-modal Heat Conduction Model and A Benchmark Dataset

Related

• Convolutional neural networks for medical image segmentation
• SUSAN: Segment Unannotated image Structure using Adversarial Network
• Medical Image Segmentation with 3D Convolutional Neural Networks: A Survey
• Convolution Neural Networks for Semantic Segmentation: Application to Small Datasets of Biomedical Images
• Technical Considerations for Semantic Segmentation in MRI using Convolutional Neural Networks