# Deep Sequential Segmentation of Organs in Volumetric Medical Scans

arXiv: https://arxiv.org/abs/1807.02437 · HTML: https://mlsanity.com/paper/1807.02437/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2018.

## Built on by (most cited first, 6 of 6)

- [Accelerating Volumetric Medical Image Annotation via Short-Long Memory SAM 2](https://mlsanity.com/paper/2505.01854/) (2025)
- [Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations](https://mlsanity.com/paper/2103.06384/) (2021)
- [No-reference denoising of low-dose CT projections](https://mlsanity.com/paper/2102.02662/) (2021)
- [Supervised Segmentation with Domain Adaptation for Small Sampled Orbital CT Images](https://mlsanity.com/paper/2107.00418/) (2021)
- [Contextual Embedding Learning to Enhance 2D Networks for Volumetric Image Segmentation](https://mlsanity.com/paper/2404.01723/) (2024)
- [Anatomy-Change-Aware Bidirectional Selective State-Space Memory for Clinically Deployed Thoracic Radiotherapy Auto-Contouring](https://mlsanity.com/paper/2609.16036/) (2026)

## Abstract (arXiv)

Segmentation in 3D scans is playing an increasingly important role in current clinical practice supporting diagnosis, tissue quantification, or treatment planning. The current 3D approaches based on convolutional neural networks usually suffer from at least three main issues caused predominantly by implementation constraints - first, they require resizing the volume to the lower-resolutional reference dimensions, second, the capacity of such approaches is very limited due to memory restrictions, and third, all slices of volumes have to be available at any given training or testing time. We address these problems by a U-Net-like architecture consisting of bidirectional convolutional LSTM and convolutional, pooling, upsampling and concatenation layers enclosed into time-distributed wrappers. Our network can either process the full volumes in a sequential manner, or segment slabs of slices on demand. We demonstrate performance of our architecture on vertebrae and liver segmentation tasks in 3D CT scans.