# Extending Pretrained Segmentation Networks with Additional Anatomical Structures

arXiv: https://arxiv.org/abs/1811.04634 · HTML: https://mlsanity.com/paper/1811.04634/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2018.

## Built on by (most cited first, 22 of 22)

- [Continual Semantic Segmentation via Repulsion-Attraction of Sparse and Disentangled Latent Representations](https://mlsanity.com/paper/2103.06342/) (2021)
- [Replay in Deep Learning: Current Approaches and Missing Biological Elements](https://mlsanity.com/paper/2104.04132/) (2021)
- [Continual Learning for Abdominal Multi-Organ and Tumor Segmentation](https://mlsanity.com/paper/2306.00988/) (2023)
- [CLIP in Medical Imaging: A Survey](https://mlsanity.com/paper/2312.07353/) (2023)
- [Uncertainty-aware Contrastive Distillation for Incremental Semantic Segmentation](https://mlsanity.com/paper/2203.14098/) (2022)
- [Universal and Extensible Language-Vision Models for Organ Segmentation and Tumor Detection from Abdominal Computed Tomography](https://mlsanity.com/paper/2405.18356/) (2024)
- [Continual Learning in Medical Image Analysis: A Comprehensive Review of Recent Advancements and Future Prospects](https://mlsanity.com/paper/2312.17004/) (2023)
- [Attribution-aware Weight Transfer: A Warm-Start Initialization for Class-Incremental Semantic Segmentation](https://mlsanity.com/paper/2210.07207/) (2022)
- [Learning Incrementally to Segment Multiple Organs in a CT Image](https://mlsanity.com/paper/2203.02100/) (2022)
- [Fairness Continual Learning Approach to Semantic Scene Understanding in Open-World Environments](https://mlsanity.com/paper/2305.15700/) (2023)
- [Tackling Catastrophic Forgetting and Background Shift in Continual Semantic Segmentation](https://mlsanity.com/paper/2106.15287/) (2021)
- [Continual Attentive Fusion for Incremental Learning in Semantic Segmentation](https://mlsanity.com/paper/2202.00432/) (2022)
- [FALCON: Fairness Learning via Contrastive Attention Approach to Continual Semantic Scene Understanding](https://mlsanity.com/paper/2311.15965/) (2023)
- [Gold Panning in Vocabulary: An Adaptive Method for Vocabulary Expansion of Domain-Specific LLMs](https://mlsanity.com/paper/2410.01188/) (2024)
- [CONDA: Continual Unsupervised Domain Adaptation Learning in Visual Perception for Self-Driving Cars](https://mlsanity.com/paper/2212.00621/) (2022)
- [Incremental Learning for Multi-organ Segmentation with Partially Labeled Datasets](https://mlsanity.com/paper/2103.04526/) (2021)
- [Half-Real Half-Fake Distillation for Class-Incremental Semantic Segmentation](https://mlsanity.com/paper/2104.00875/) (2021)
- [Continual Segmentation with Disentangled Objectness Learning and Class Recognition](https://mlsanity.com/paper/2403.03477/) (2024)
- [Dynamic Robot-Assisted Surgery with Hierarchical Class-Incremental Semantic Segmentation](https://mlsanity.com/paper/2508.01713/) (2025)
- [Modeling the Background for Incremental and Weakly-Supervised Semantic Segmentation](https://mlsanity.com/paper/2201.13338/) (2022)
- [BACS: Background Aware Continual Semantic Segmentation](https://mlsanity.com/paper/2404.13148/) (2024)
- [CFSSeg: Closed-Form Solution for Class-Incremental Semantic Segmentation of 2D Images and 3D Point Clouds](https://mlsanity.com/paper/2412.10834/) (2024)

## Abstract (arXiv)

Comprehensive surgical planning require complex patient-specific anatomical models. For instance, functional muskuloskeletal simulations necessitate all relevant structures to be segmented, which could be performed in real-time using deep neural networks given sufficient annotated samples. Such large datasets of multiple structure annotations are costly to procure and are often unavailable in practice. Nevertheless, annotations from different studies and centers can be readily available, or become available in the future in an incremental fashion. We propose a class-incremental segmentation framework for extending a deep network trained for some anatomical structure to yet another structure using a small incremental annotation set. Through distilling knowledge from the current state of the framework, we bypass the need for a full retraining. This is a meta-method to extend any choice of desired deep segmentation network with only a minor addition per structure, which makes it suitable for lifelong class-incremental learning and applicable also for future deep neural network architectures. We evaluated our methods on a public knee dataset of 100 MR volumes. Through varying amount of incremental annotation ratios, we show how our proposed method can retain the previous anatomical structure segmentation performance superior to the conventional finetuning approach. In addition, our framework inherently exploits transferable knowledge from previously trained structures to incremental tasks, demonstrated by superior results compared to non-incremental training. With the presented method, new anatomical structures can be learned without catastrophic forgetting of older structures and without extensive increase of memory and complexity.