Organ at Risk Segmentation for Head and Neck Cancer using Stratified Learning and Neural Architecture Search

04/17/2020
by   Dazhou Guo, et al.
6

OAR segmentation is a critical step in radiotherapy of head and neck (H N) cancer, where inconsistencies across radiation oncologists and prohibitive labor costs motivate automated approaches. However, leading methods using standard fully convolutional network workflows that are challenged when the number of OARs becomes large, e.g. > 40. For such scenarios, insights can be gained from the stratification approaches seen in manual clinical OAR delineation. This is the goal of our work, where we introduce stratified organ at risk segmentation (SOARS), an approach that stratifies OARs into anchor, mid-level, and small hard (S H) categories. SOARS stratifies across two dimensions. The first dimension is that distinct processing pipelines are used for each OAR category. In particular, inspired by clinical practices, anchor OARs are used to guide the mid-level and S H categories. The second dimension is that distinct network architectures are used to manage the significant contrast, size, and anatomy variations between different OARs. We use differentiable neural architecture search (NAS), allowing the network to choose among 2D, 3D or Pseudo-3D convolutions. Extensive 4-fold cross-validation on 142 H N cancer patients with 42 manually labeled OARs, the most comprehensive OAR dataset to date, demonstrates that both pipeline- and NAS-stratification significantly improves quantitative performance over the state-of-the-art (from 69.52 principled means to manage the highly complex segmentation space of OARs.

READ FULL TEXT

page 1

page 3

page 6

page 7

page 12

research
06/06/2019

V-NAS: Neural Architecture Search for Volumetric Medical Image Segmentation

Deep learning algorithms, in particular 2D and 3D fully convolutional ne...
research
07/07/2020

GOLD-NAS: Gradual, One-Level, Differentiable

There has been a large literature of neural architecture search, but mos...
research
11/08/2021

Approximate Neural Architecture Search via Operation Distribution Learning

The standard paradigm in Neural Architecture Search (NAS) is to search f...
research
11/29/2019

Blockwisely Supervised Neural Architecture Search with Knowledge Distillation

Neural Architecture Search (NAS), aiming at automatically designing netw...
research
09/03/2019

MANAS: Multi-Agent Neural Architecture Search

The Neural Architecture Search (NAS) problem is typically formulated as ...
research
02/04/2022

Heed the Noise in Performance Evaluations in Neural Architecture Search

Neural Architecture Search (NAS) has recently become a topic of great in...

Please sign up or login with your details

Forgot password? Click here to reset