# An application of cascaded 3D fully convolutional networks for medical image segmentation

arXiv: https://arxiv.org/abs/1803.05431 · HTML: https://mlsanity.com/paper/1803.05431/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2018.

## Built on by (most cited first, 30 of 34)

- [AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation](https://mlsanity.com/paper/2206.08023/) (2022)
- [Robust machine learning segmentation for large-scale analysis of heterogeneous clinical brain MRI datasets](https://mlsanity.com/paper/2209.02032/) (2022)
- [Few-shot Medical Image Segmentation with Cycle-resemblance Attention](https://mlsanity.com/paper/2212.03967/) (2022)
- [Deep Learning Based 3D Segmentation: A Survey](https://mlsanity.com/paper/2103.05423/) (2021)
- [Vision Transformers in Medical Computer Vision -- A Contemplative Retrospection](https://mlsanity.com/paper/2203.15269/) (2022)
- [MagicNet: Semi-Supervised Multi-Organ Segmentation via Magic-Cube Partition and Recovery](https://mlsanity.com/paper/2212.14310/) (2022)
- [Medical Image Segmentation with 3D Convolutional Neural Networks: A Survey](https://mlsanity.com/paper/2108.08467/) (2021)
- [OSS-Net: Memory Efficient High Resolution Semantic Segmentation of 3D Medical Data](https://mlsanity.com/paper/2110.10640/) (2021)
- [Learning Inductive Attention Guidance for Partially Supervised Pancreatic Ductal Adenocarcinoma Prediction](https://mlsanity.com/paper/2105.14773/) (2021)
- [Large-Kernel Attention for 3D Medical Image Segmentation](https://mlsanity.com/paper/2207.11225/) (2022)
- [Robust Segmentation of Brain MRI in the Wild with Hierarchical CNNs and no Retraining](https://mlsanity.com/paper/2203.01969/) (2022)
- [Improved Abdominal Multi-Organ Segmentation via 3D Boundary-Constrained Deep Neural Networks](https://mlsanity.com/paper/2210.04285/) (2022)
- [Deep Learning for Pancreas Segmentation: a Systematic Review](https://mlsanity.com/paper/2407.16313/) (2024)
- [Autoregressive Sequence Modeling for 3D Medical Image Representation](https://mlsanity.com/paper/2409.08691/) (2024)
- [Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations](https://mlsanity.com/paper/2103.06384/) (2021)
- [Recurrent Feature Propagation and Edge Skip-Connections for Automatic Abdominal Organ Segmentation](https://mlsanity.com/paper/2201.00317/) (2022)
- [Deep and Statistical Learning in Biomedical Imaging: State of the Art in 3D MRI Brain Tumor Segmentation](https://mlsanity.com/paper/2103.05529/) (2021)
- [A New Probabilistic V-Net Model with Hierarchical Spatial Feature Transform for Efficient Abdominal Multi-Organ Segmentation](https://mlsanity.com/paper/2208.01382/) (2022)
- [Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotation](https://mlsanity.com/paper/2302.03296/) (2023)
- [CTG-Net: An Efficient Cascaded Framework Driven by Terminal Guidance Mechanism for Dilated Pancreatic Duct Segmentation](https://mlsanity.com/paper/2303.02944/) (2023)
- [Deep Learning for Vascular Segmentation and Applications in Phase Contrast Tomography Imaging](https://mlsanity.com/paper/2311.13319/) (2023)
- [Brain Tumor Segmentation Based on Deep Learning, Attention Mechanisms, and Energy-Based Uncertainty Prediction](https://mlsanity.com/paper/2401.00587/) (2023)
- [COVID-19 Infection Segmentation from Chest CT Images Based on Scale Uncertainty](https://mlsanity.com/paper/2201.03053/) (2022)
- [Large Language Model-Augmented Auto-Delineation of Treatment Target Volume in Radiation Therapy](https://mlsanity.com/paper/2407.07296/) (2024)
- [OARnet: Automated organs-at-risk delineation in Head and Neck CT images](https://mlsanity.com/paper/2108.13987/) (2021)
- [A State-of-the-art Survey of U-Net in Microscopic Image Analysis: from Simple Usage to Structure Mortification](https://mlsanity.com/paper/2202.06465/) (2022)
- [Pseudo-Label Guided Multi-Contrast Generalization for Non-Contrast Organ-Aware Segmentation](https://mlsanity.com/paper/2205.05898/) (2022)
- [Immunofluorescence Capillary Imaging Segmentation: Cases Study](https://mlsanity.com/paper/2207.06861/) (2022)
- [Cutting-Splicing data augmentation: A novel technology for medical image segmentation](https://mlsanity.com/paper/2210.09099/) (2022)
- [Ensemble Methods for Multi-Organ Segmentation in CT Series](https://mlsanity.com/paper/2303.17956/) (2023)

## Abstract (arXiv)

Recent advances in 3D fully convolutional networks (FCN) have made it feasible to produce dense voxel-wise predictions of volumetric images. In this work, we show that a multi-class 3D FCN trained on manually labeled CT scans of several anatomical structures (ranging from the large organs to thin vessels) can achieve competitive segmentation results, while avoiding the need for handcrafting features or training class-specific models. To this end, we propose a two-stage, coarse-to-fine approach that will first use a 3D FCN to roughly define a candidate region, which will then be used as input to a second 3D FCN. This reduces the number of voxels the second FCN has to classify to ~10% and allows it to focus on more detailed segmentation of the organs and vessels. We utilize training and validation sets consisting of 331 clinical CT images and test our models on a completely unseen data collection acquired at a different hospital that includes 150 CT scans, targeting three anatomical organs (liver, spleen, and pancreas). In challenging organs such as the pancreas, our cascaded approach improves the mean Dice score from 68.5 to 82.2%, achieving the highest reported average score on this dataset. We compare with a 2D FCN method on a separate dataset of 240 CT scans with 18 classes and achieve a significantly higher performance in small organs and vessels. Furthermore, we explore fine-tuning our models to different datasets. Our experiments illustrate the promise and robustness of current 3D FCN based semantic segmentation of medical images, achieving state-of-the-art results. Our code and trained models are available for download: https://github.com/holgerroth/3Dunet_abdomen_cascade.