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Artificial Intelligence in Adaptive Radiation Therapy
images for dose calculation, image segmentation, registration and PET attenuation
correction. While feasibility has been demonstrated in recent studies, addressing
challenges remains pivotal prior to clinical implementation.
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IOP Publishing
Artificial Intelligence in Adaptive Radiation Therapy
Yi Wang and X. Sharon Qi
Chapter 9
Artificial intelligence-based image registration
and segmentation
Brian M Anderson and Kristy K Brock
This chapter delves into the integration of artificial intelligence (AI) into adaptive
radiation therapy (ART), emphasizing AI-based image registration and segmentation. It begins by introducing ART’ s three categories—offline, online, and real-time
—and the challenges they pose, such as accurately adapting treatment plans to
patient-specific changes. The chapter then explains fundamental AI concepts,
including machine learning and deep learning, highlighting components such as
convolutional neural networks and activation functions. It explores how these AI
techniques enhance image registration and segmentation, which are crucial for
precise targeting and dose calculation in ART. By addressing both the potential
benefits and challenges of AI in this context, the chapter provides insights into how
AI can optimize ART workflows and improve personalized patient care.
9.1 AI-based image registration and segmentation for ART
9.1.1 Adaptive radiation therapy
Historically in radiation therapy, a single radiation treatment plan is generated for
an individual patient, which will be used for the duration of care [1]. Adaptive
radiation therapy (ART) is a broad umbrella which encompasses any changes from
this single radiation treatment plan regiment, based on changes present in the
patient. These changes in the patient can be inter-fractional (reduction/increase in
tumor burden, inflammation, changes in bowel location/filling, bladder filling, etc)
or intra-fractional (respiratory motion, cardiac motion, peristalsis, skeletal motion).
ART can be categorized into three groups: offline ART, online ART, and realtime ART. All these approaches have the potential to dose escalate and/or reduce
normal tissue dose [2–4]. However, they also raise a number of challenges and
difficulties which might require accurate segmentation of target and tissues at risk
(segmentation), recalculation of dose and summation of previously delivered
doi:10.1088/978-0-7503-6119-4ch9 9-1 ª IOP Publishing Ltd 2025. All rights,
including for text and data mining (TDM), artificial intelligence (AI) training, and similar technologies, are reserved.
Artificial Intelligence in Adaptive Radiation Therapy
treatments (via deformable registration) re-planning based on patient geometry, and
quality assurance ensuring safe adaptation of a plan.
9.1.1.1 Offline ART
Offline ART is commonly performed as part of routine clinical practice in many
radiation oncology clinics. This involves the evaluation of imaging (daily or weekly)
acquired throughout a patient’s treatment for changes in normal tissue and tumor
volumes. Patients with bulky disease that is expected to diminish throughout
treatment can often be scheduled proactively for mid-treatment simulation scans,
e.g. evaluation at fraction 15, or on an ad hoc basis driven by assessments at time of
treatment: such as increased difficulty with patient setup. There are several examples
of success in dose escalation/normal tissue sparing seen by multiple clinics using
offline ART [5–8].
9.1.1.2 Online ART
Online ART is focused on adapting a treatment plan based on inter-fractional
changes while the patient is on the table/in the treatment position. There are several
successful examples with active online ART programs for multiple treatment sites
[9–17]. This has the added difficulty of a compressed time frame for many of the
steps listed above.
When adapting and recalculating a new treatment plan, a major consideration is the
ability to accurately calculate dose on the image acquired in-room during treatment.
The Varian Ethos (Varian Medical Systems, Palo Alto, CA) system focuses on CBCTguided adaptive radiation therapy [18]. The Elekta Unity (Elekta Stockholm, Sweden)
1.5 T MR linac system and the ViewRay MRIdian 0.35 T MR linac system focuses on
using an integrated MR linac to visualize changes occurring throughout the treatment
process and can be used for both online [19] and real-time ART [20].
9.1.1.3 Real-time ART
Real-time ART aims to account for intra-fractional changes occurring during the
patient treatment and enable automatic adjustments to optimize delivery/treat the
target. This is not limited to the definition of a complete re-planning for the patient,
but can include respiratory gating [21], MLC target tracking via KV monitoring [22]
and implanted fiducials [23] (CyberKnife system), MR imaging [24] (with both
Elekta Unity and ViewRay MRIdian), and external surface tracking.
9.1.1.4 Challenges
As stated previously, ART requires a concert of moving parts to either acquire a new
planning image or adapt the daily image for dose accumulation, segmentation of
targets and normal tissues for dose evaluation, accumulation of previously delivered
radiation on the new image, perform re-planning, and quality assurance. To
expedite these time restricted steps, AI is often leveraged to register, deform, and
propagate not only previously defined contours, but also accumulate previously
delivered dose. In the following sections we will discuss AI in registration (rigid and
deformable), image segmentation, and discuss how these solutions can help alleviate
some of these challenges, while creating some new ones as well.
9-2
Artificial Intelligence in Adaptive Radiation Therapy
9.2 Artificial intelligence
What defines artificial intelligence (AI)? While the phrase AI can sometimes feel like a
creation of the twenty-first century, it is something which has been a part of the user
experience with computers for quite some time. AI can be defined as anything which
enables computers to mimic human behavior. This behavior can be something as
simple as identifying a spam email, or as complex as diagnosing a patient’s disease. A
more extensive explanation of both machine learning and deep learning is beyond the
immediate scope of the information presented later in this chapter. For a mathematical explanation of deep learning, particularly as it applies to deep learning in Python,
we highly recommend the books Deep Learning by Ian Goodfellow et al [25] and Deep
Learning with Python [26].
9.2.1 What is machine learning?
Within the broad scope of AI there exists a smaller distinction known as machine
learning. Machine learning can be defined as an AI model which is generated from a
loss-minimization or separation. A model ‘loss’ function can be imaged as a measure
of how incorrect the model is; hence, we often express the desire to ‘minimize loss’.
For example, imagine a model is created to separate Honeycrisp apples (a reddishyellow sweet apple) from Granny Smith apples (a green, sour apple). The user could
create a variety of features (size, taste, color) that they feel can be quantified between
the two apples. A machine learning model would then try to use these features to
separate the apples into two groups. A very basic idea of this is shown on the left in
figure 9.1, where the features are used directly to make an output prediction.
Figure 9.1. (Left) Basic level of a machine learning model: combining input features to an output. (Right) Basic
level of a ‘shallow’ learning model: combining features with some number of hidden layers into an output.
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