‘Skeleton Recall Loss’ Is The New Breakthrough In Segmentation

A deep dive into how conventional Segmentation works and how the ‘Skeleton Recall Loss’ sets itself as the new state-of-the-art in Thin-structure Segmentation

Image generated with DALL-E 3

Precise segmentation is a critical requirement across many domains today.

These include training self-driving cars, medical image recognition systems, and monitoring using satellite imagery, to name a few.

Further precision is needed in many other fields where the objects of interest are minuscule but critical, such as studying blood vessel flow, surgical planning, detecting cracks in architectural structures, or optimizing route planning.

A lot of work has been previously done to address such challenging segmentation.

This includes mathematical techniques like:

• Image transforms

• Mathematical morphologies

• Filtering

• Differential operators

Furthermore, advancements in deep learning neural networks such as U-Nets and their variations have greatly enhanced segmentation accuracy.

“What’s the issue then?”— you’d ask.

All of these methods poorly segment tiny, elongated and curvilinear structures.

And the loss function involved might be one to blame.