# Robust rigid MRI-TRUS registration in prostate cancer using attention-CNN and ICP

Manasi Kattel, Benjamin Billot, Federica Facente, Hervé Delingette, Nicholas Ayache

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Manasi Kattel, Benjamin Billot, Federica Facente, Hervé Delingette, Nicholas Ayache. Robust rigid MRI-TRUS registration in prostate cancer using attention-CNN and ICP. 6th International Workshop of Advances in Simplifying Medical UltraSound (ASMUS), Sep 2025, Daejeon, South Korea. hal-05218125

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Distributed under a Creative Commons Attribution 4.0 International License
Robust rigid MRI-TRUS registration in prostate
cancer using attention-CNN and ICP

Manasi Kattel, Benjamin Billot, Federica Facente,
Hervé Delingette, and Nicholas Ayache

Université Côte d'Azur, Inria, Epione team, France
manasi.kattel@inria.fr

**Abstract.** Transrectal ultrasound (TRUS) image-guidance is crucial in monitoring prostate cancer by enabling precise lesion targeting for biopsy and radiotherapy. Image-guided navigation can be further improved by manually overlaying a 3D preoperative MRI scan to benefit from its superior contrast and resolution. Here we propose to automate this procedure, which we formulate as MRI-TRUS 3D rigid registration. However, this task faces three challenges: the large domain gap between MRI and TRUS; the relative spherical symmetry of the prostate that complicates rotation estimation; the potential large misalignments due to the wide range of ultrasound probe orientations relative to the MR coordinate frame. Here we present MUReg, a new pipeline for MRI-TRUS rigid registration. First, we initialize translations by aligning the prostate centers of mass derived from segmentations. Then, we regress rotations with an attention-based CNN that takes segmentations as inputs (to eliminate the domain gap) and that is trained with a novel loss penalizing displacement errors to alleviate the symmetry issue. Finally, we enhance the robustness of these initial predictions by using an iterative closest point algorithm. Overall, MUReg significantly outperforms state-of-the-art optimization and deep learning methods on four different metrics.

**Keywords:** MRI-TRUS rigid registration, learning-based regression, ICP

# 1 Introduction

Prostate cancer is the second most frequent cancer in men worldwide [6]. Hence, accurate lesion characterization is paramount in enhancing early detection and treatment planning, as well as preventing over-diagnosis. Biopsy is the gold standard in prostate cancer diagnosis, and is often guided with real-time intraoperative transrectal ultrasound (TRUS). However, traditional TRUS-guided biopsies often rely on systematic sampling, which can miss clinically significant tumors [22]. For this reason, it is frequent to acquire a preoperative magnetic resonance image (MRI), whose superior voxel resolution and tissue contrast helps visualizing lesions during biopsy planning. However, these lesions can only be accurately reached during biopsy by directly overlaying the MRI-defined targets onto the intraoperative TRUS [7]. As such, this problem can be formulated as 3D multi-modal MRI-TRUS registration, and has been an active area of research.
Although MRI-TRUS registration can be tackled with usual optimization methods [1], these lack a similarity metric that is robust to such a large domain gap. A first solution is to use neighborhood descriptors [12], which has been done in the context of MRI-regular ultrasound (US) alignment [13]. Meanwhile, the LC2 metric has been proposed to combine intensity and gradient information from MRI and US [24,30]. However, in addition to being slow, these approaches are prone to local minima and require close initialization. While DISA-LC2 has been proposed to enhance robustness by introducing a learned approximation of LC2 [25], it still requires a reasonably close initialization. Overall, the lack of robustness limits the application of optimization-based methods to MRI-TRUS registration, where the ultrasound probe can be oriented at various angles.

Modern strategies for MRI-TRUS registration now almost exclusively rely on convolutional neural networks (CNNs). A first class of methods treats this problem as regression, where an early approach has proposed to regress a similarity metric between anatomical landmarks by only taking MRI-TRUS pairs as inputs, and using this metric within an optimization framework [10]. A more straightforward solution is to directly regress ground truth transforms derived from landmarks annotated in both modalities [14,28]. Other regression approaches have proposed to replace this cumbersome supervision with segmentations used to compute a Dice loss between moved and fixed labels, where the network inputs are either images in the case of non-linear registration [3,16,31], or segmentations in the case of rigid transforms (which also eliminates the domain gap issue) [5,33]. However, predicting accurate transforms in only one step remains challenging (especially for rigid registration), which limits the robustness of regression methods against large misalignments. For this reason, other works have formulated MRI-TRUS registration as a supervised landmark detection problem [9], where rigid transforms are then estimated in closed-form [15]. Finally, a recent strategy directly tackles the MRI-TRUS registration problem at its root, i.e., the domain shift issue, by performing MRI-to-TRUS image translation [2]. However, this technique is limited by the image-to-image translation task, which is an active area of research for general MRI-ultrasound translation [8].

We present a new method named MUReg for MRI-TRUS rigid registration. Specifically, MUReg uses a three-step approach: we first initialize the translation by aligning the prostate centers of mass derived from segmentations obtained by modality-specific segmentation CNNs; we predict the rotation component using a regression CNN; and we robustly refine this rigid pre-alignment with an efficient variant of the iterative closest point (ICP) algorithm [27]. Importantly, we particularly focus on rotation estimation, which is very challenging due to the prostate spherical symmetry. In this purpose, our rotation CNN (i) is trained with a novel loss called BBVRE (Bounding Box Vertex Registration Error), which focuses on displacement error to alleviate the spherical symmetry (ii) takes masks as inputs to mitigate the domain gap, (iii) regresses a 6D angle representation [35] and leverages the same attention block as [28] for robust predictions. Overall, MUReg significantly outperforms state-of-the-art optimization and learning-based methods specifically developed for MRI-TRUS rigid registration.
<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Overview of MUReg, a pipeline for 3D MRI-TRUS rigid registration. We first obtain prostate masks with two CNNs, and use the centers of mass to initialize the translation. Then, we regress the rotation using an attention-CNN with masks as inputs. Finally, we refine the obtained rigid transform with ICP to improve robustness.</figcaption></figure>

## 2 Methods

The proposed MUReg pipeline is illustrated in Figure 1. Our goal is to spatially align a 3D MRI scan onto a TRUS volume by finding a rigid transform $T = [R, t]$, consisting of a rotation matrix $R \in \mathbb{R}^{3 \times 3}$ and a translation vector $t \in \mathbb{R}^3$. MUReg counts three distinct steps. First, we train two segmentation CNNs (one for each modality) with the purpose of initializing translations. Second, we train an attention-CNN to regress the rotation. Finally, we refine the resulting preliminary rigid transform by using ICP to enhance prediction robustness.

### 2.1 Translation initialization

Let $f_{\text{MRI}}$ and $f_{\text{TRUS}}$ be two CNNs trained to segment the prostate from input MRI and TRUS volumes $V_{\text{MRI}}$ and $V_{\text{TRUS}}$, respectively. Let us denote their output binary masks as $S_{\text{MRI}} = f_{\text{MRI}}(V_{\text{MRI}})$ and $S_{\text{TRUS}} = f_{\text{TRUS}}(V_{\text{TRUS}})$. Finally, let $c_{\text{MRI}}$ and $c_{\text{TRUS}} \in \mathbb{R}^3$ represent the centers of mass of the prostate masks in $S_{\text{MRI}}$ and $S_{\text{TRUS}}$, respectively. Here, we initialize the translation with $t = c_{\text{MRI}} - c_{\text{TRUS}}$. Applying $t$ to $V_{\text{MRI}}$ aligns its center of mass to $c_{\text{TRUS}}$.

### 2.2 Rotation estimation and associated BBVRE loss

After having aligned the two modalities at the center of the prostate, we now focus on estimating the rotation $R$. In this purpose, we train a CNN regressor $f_r$ to take $S_{\text{MRI}}$ and $S_{\text{TRUS}}$ as inputs and to predict a rotation $r = f_r(S_{\text{MRI}}, S_{\text{TRUS}}) \in \mathbb{R}^6$. Here, we emphasize that using prostate masks as inputs is sufficient for *rigid* registration (as our results will show) and enables us to alleviate the domain gap issue. We also highlight that $f_r$ does not regress the matrix $R$ directly, but rather a fully continuous 6D parametrization $r$ that has been shown to be more suitable for learning [35], including in the context of rigid MRI-TRUS registration [14]. Specifically, we split $r$ into two $\mathbb{R}^3$ vectors $r = [r_1, r_2]$ and decompose
<figure><img src="image_3.png" /><figcaption>Fig. 2. Proposed BBVRE loss between a predicted rigid transform $T$ and its ground truth $T_{GT}$. BBVRE computes the average distance between bounding box vertices $V = \{v_1, \dots, v_8\}$ before and after applying $T$ (orange) and $T_{GT}^{-1}$ (blue).</figcaption></figure>

$R$ into its column vectors $[R_1, R_2, R_3]$. If $\|\cdot\|$ is the Euclidean distance and $^\top$ is the transpose operator, then $R$ is given by Gram-Schmidt-like normalization [35]:

$$R_1 = \frac{r_1}{\|r_1\|}, \quad R_2 = \frac{r_2 - (R_1^\top r_2)R_1}{\|r_2 - (R_1^\top r_2)R_1\|}, \quad R_3 = R_1 \times R_2. \quad (1)$$

Importantly, we propose here to optimize $f_r$ with a novel loss named BBVRE (Bounding Box Vertex Registration Error), which measures the mean displacement error when transforming a set of reference points defined as the prostate bounding box corners (directly computed from $S_{MRI}, S_{TRUS}$). More precisely, given a set of 8 vertices of a 3D bounding box $V = \{v_1, v_2, \dots, v_8\}$, we first apply the estimated rotation $R$, followed by the inverse of the ground truth rotation $R_{GT}^{-1}$, and then compute the Euclidean distance between the original and recovered points (see illustration in Figure 2). Moreover, to ensure that BBVRE is not biased by subject-specific prostate size, we normalize it by the average prostate size in the training dataset. In this purpose, we rescale the bounding box such that the length $d$ of its diagonal matches the corresponding average value $\bar{d}$ across the training set. Overall, BBVRE is defined as:

$$\text{BBVRE} = \frac{\bar{d}}{d} \times \frac{1}{8} \sum_{i=1}^{8} \|v_i - R_{GT}^{-1} R v_i\|. \quad (2)$$

The purpose of the BBVRE loss is to penalize misalignments in terms of displacement rather than directly optimizing angle error (with geodesic [17] and MSE [14,28] losses) or segmentation alignment (with Dice loss [3,16,31]), which can suffer from the relative spherical symmetry of the prostate.

## 2.3 ICP refinement

Direct regression of rigid transforms is generally very challenging, which explains why the vast majority of traditional optimization strategies use iterative approaches [1,21]. Building on this idea, we propose here to refine our first estimation of $T = [R, t]$ by using an efficient variant of the ICP algorithm [27].
This algorithm operates on prostate meshes extracted from the moved MRI and fixed TRUS masks by minimizing the distance between pairs of points (pairs are assigned by projection matching). More precisely, at each iteration, the ICP algorithm refines the estimate $T = [R, t]$ by optimizing in closed-form [29] the following objective function [27]:

$$ \arg\min_{[R,t]} \sum_i \|RP_{\text{MRI},i} + t - P_{\text{TRUS},i}\|^2, \quad (3) $$

where $P_{\text{TRUS},i}$ and $P_{\text{MRI},i}$ are the $i^{th}$ pair of corresponding points between the fixed TRUS and MRI prostate meshes. The ICP stops upon reaching a $10^{-5}$ change in cost function, or after 100 iterations.

## 2.4 Implementation details

**Segmentation networks:** The segmentation CNNs $f_{\text{MRI}}$ and $f_{\text{TRUS}}$ are trained as 3D UNet models [26] implemented using nnU-Net [18] with self-configured pre-processing, augmentation, architecture, training procedure, and postprocessing.

**Rotation network:** The architecture of $f_r$ starts as a dual-stream where both modalities are processed by identical encoders. These consist of two blocks, each with a convolution layer, group normalization [32], LeakyReLU [11], and max-pooling. Importantly, the resulting features are passed through a cross-modal attention module to better capture interactions between the MRI and TRUS features [28]. These features are then concatenated and fed to three additional convolutional blocks followed by four fully connected layers to regress $r$. In addition to the initial rotation misalignment, training masks are augmented with 3D rotations uniformly sampled from $[-10, 10]^{\circ}$. $f_r$ is implemented in PyTorch [23]. Training and inference take 40h and 1.1s (Nvidia GTX 1080, 24Gb), respectively.

**ICP:** Meshes are extracted from MRI and TRUS prostate masks using marching cubes [20]. ICP is implemented with Open3D [34] and takes 1.2s for inference.

# 3 Experiments and results

## 3.1 Dataset

In this paper, we use an in-house dataset of 1247 subjects, who have undergone TRUS-guided biopsy for prostate cancer. Each subject comes with a 3D preoperative T2-weighted MRI and an intraoperative TRUS volume. Importantly, these are acquired on an MRI-TRUS fusion biopsy system (Urostation and Trinity from Koelis SAS with End-Fire probe), where urologists have manually annotated prostate masks as well as ground truth rigid transforms for each MRI-TRUS pair. MRI and TRUS volumes are acquired with mean 0.4mm isotropic resolution and average volume size of 320×320×160 and 256×256×256 respectively. After segmentation, we resample all volumes to 1.4mm isotropic resolution and center-crop them around the prostate to a 64×64×64 size. We use training/validation/testing splits of 1000/100/147 MRI-TRUS pairs, respectively.
<figure><img src="image_4.png" /><figcaption>**Fig. 3.** Examples of rigid registration results obtained by all baselines on two representative subjects (one per row). Contours of the MRI/TRUS prostate masks are given in green/orange, respectively, and their centers of mass are marked with × of corresponding color. Red arrows point at major mistakes.</figcaption></figure>

## 3.2 Baselines

**DISA-LC2** [25] is an optimization technique for MRI-TRUS registration that approximates the previously proposed LC2 metric [30] with a differentiable variant using features learned by a CNN.

**MIND-SSC** [13] is an MRI-TRUS optimization-based registration method that extracts modality-independent features [12] and optimizes their distance.

**CNNReg** [14] uses a CNN trained with an MSE loss to regress rigid transforms by processing the input MR and TRUS volumes with separate encoders before passing the concatenated features through fully connected layers.

**AttentionReg** [28] yields state-of-the-art results in MRI-TRUS rigid registration by using a similar regressor as CNNReg with an added attention module.

MIND-SSC and DISA-LC2 are optimized using Adam [19]. The learning methods are retrained on our data with their default implementation. For fair comparison, all methods use the same preprocessing, translation initialization, 6D rotation parameterization [35], and encoder architecture (for CNNReg and AttentionReg) as MUReg, which consistently improves validation scores.

## 3.3 Evaluation metrics

We evaluate segmentation accuracy using Dice, mean surface distance (MSD), and the 99th percentile of surface distance (HD99). Registration performance is assessed with rotation and translation errors, BBVRE, and surface registration error (SRE) given by $SRE = \frac{1}{N} \sum_{n=1}^{N} \|T_{GT}^{-1}(x_n) - T^{-1}(x_n)\|$, where $\{x_n\}_{n=1}^N$ are N surface points from MRI prostate mesh, and $T$ and $T_{GT}$ are the predicted and ground truth transforms. Moreover, although reporting Dice is not necessarily
<table><caption>**Table 1.** Mean scores (standard deviations) obtained by the MRI and TRUS prostate segmentation CNNs $f_{\text{MRI}}$ and $f_{\text{TRUS}}$, respectively. Distances are expressed in mm.</caption><thead><tr><th>Modality</th><th>Dice (↑)</th><th>MSD (↓)</th><th>HD99 (↓)</th></tr></thead><tbody><tr><td>MRI Prostate</td><td>0.91 (0.04)</td><td>0.12 (0.21)</td><td>2.05 (2.01)</td></tr><tr><td>TRUS Prostate</td><td>0.92 (0.03)</td><td>0.14 (0.24)</td><td>2.18 (1.92)</td></tr></tbody></table>

<table><caption>**Table 2.** Mean scores (standard deviations) for all methods and ablations. Distances are in mm. Best scores are in bold. We run Bonferroni-corrected Wilcoxon signed-rank test (1% level): * denotes significance between MUReg and all baselines; while – marks significance between MUReg without ICP and the associated ablations.</caption><thead><tr><th>Method</th><th>Rot. Err. (°)</th><th>Transl. Err.</th><th>SRE</th><th>BBVRE</th><th>Dice</th></tr></thead><tbody><tr><td>Without alignment</td><td>30.3 (11.2)</td><td>22.1 (11.2)</td><td>23.9 (10.8)</td><td>41.4 (16.3)</td><td>0.64 (0.16)</td></tr><tr><td colspan="6">Comparison with baselines</td></tr><tr><td>DISA-LC2 [25]</td><td>27.9 (17.0)</td><td>9.63 (10.6)</td><td>13.6 (10.2)</td><td>26.9 (15.9)</td><td>.66 (.21)</td></tr><tr><td>MIND-SSC [13]</td><td>25.1 (13.9)</td><td>5.40 (4.18)</td><td>9.86 (4.49)</td><td>21.3 (9.46)</td><td>.75 (.12)</td></tr><tr><td>CNNReg [14]</td><td>12.2 (6.09)</td><td>2.03 (1.36)</td><td>4.32 (1.82)</td><td>9.87 (4.26)</td><td>.85 (.03)</td></tr><tr><td>AttentionReg [28]</td><td>11.9 (6.16)</td><td>2.02 (1.31)</td><td>4.25 (1.81)</td><td>9.69 (4.32)</td><td>.85 (.03)</td></tr><tr><td>MUReg (ours)</td><td><b>9.67 (5.51)*</b></td><td><b>1.90 (1.20)*</b></td><td><b>3.63 (1.80)*</b></td><td><b>8.09 (3.95)*</b></td><td>.85 (.03)</td></tr><tr><td colspan="6">Ablations on MUReg</td></tr><tr><td>MUReg w/o ICP</td><td>9.82 (5.67)</td><td>2.02 (1.35)</td><td>3.76 (1.81)</td><td>8.31 (4.01)</td><td>.85 (.03)</td></tr><tr><td>ICP only</td><td>23.0 (10.7)<sup>-</sup></td><td>1.95 (1.42)<sup>-</sup></td><td>7.68 (3.94)<sup>-</sup></td><td>17.9 (8.53)<sup>-</sup></td><td>.84 (.03)<sup>-</sup></td></tr><tr><td>Single T regr. ⊕ ICP</td><td>10.64 (5.67)<sup>-</sup></td><td>3.07 (1.75)<sup>-</sup></td><td>4.43(2.42)<sup>-</sup></td><td>9.22 (4.36)<sup>-</sup></td><td>.82 (.04)<sup>-</sup></td></tr><tr><td colspan="6">Ablations on MUReg without ICP (i.e., f<sub>r</sub>)</td></tr><tr><td>Dice loss</td><td>10.4 (5.10)<sup>-</sup></td><td>2.02 (1.35)</td><td>3.91 (1.75)<sup>-</sup></td><td>8.59 (3.95)<sup>-</sup></td><td>.85 (.03)</td></tr><tr><td>MSE loss</td><td>10.0 (5.48)</td><td>2.02 (1.35)</td><td>3.82 (1.83)</td><td>8.44 (3.96)</td><td>.85 (.03)</td></tr><tr><td>Geodesic loss</td><td>9.97 (5.59)</td><td>2.02 (1.35)</td><td>3.82 (1.91)</td><td>8.41 (4.03)</td><td>.85 (.03)</td></tr><tr><td>MRI seg./TRUS im.</td><td>10.8 (5.52)<sup>-</sup></td><td>2.02 (1.35)</td><td>4.01 (1.93)<sup>-</sup></td><td>8.90 (4.04)</td><td>.85 (.03)</td></tr><tr><td>MRI im./TRUS im.</td><td>12.1 (6.14)<sup>-</sup></td><td>2.02 (1.35)</td><td>4.33 (1.84)<sup>-</sup></td><td>9.86 (4.33)<sup>-</sup></td><td>.85 (.03)</td></tr><tr><td>f<sub>r</sub> w/o attention</td><td>10.0 (5.74)</td><td>2.02 (1.35)</td><td>3.84 (1.93)</td><td>8.45 (4.14)<sup>-</sup></td><td>.85 (.03)</td></tr><tr><td>f<sub>r</sub> with single enc.</td><td>10.2 (5.45)</td><td>2.02 (1.35)</td><td>3.87 (1.89)</td><td>8.55 (3.93)<sup>-</sup></td><td>.85 (.03)</td></tr></tbody></table>

reliable to evaluate rotation predictions in the case of relative spherical symmetry [4], we also report it for consistency with the literature [16,31,33].

## 3.4 Results

**Segmentation performance** We first evaluate the performance of the segmentation networks $f_{\text{MRI}}$ and $f_{\text{TRUS}}$, which form the basis of the proposed MRI-TRUS registration pipeline. Overall, Table 1 shows that both networks are able to accurately segment the prostate, with $f_{\text{MRI}}$ and $f_{\text{TRUS}}$ both yielding mean Dice scores over 0.91 and mean MSD below 0.14mm.

**Registration performance** Table 2 summarizes the scores obtained by all competitors. Remarkably, MUReg recovers from the large initial misalignments by bringing the rotation/translation errors from 30.3°/22.1mm to 9.67°/1.90mm, respectively. This is better than the state-of-the-art MRI-TRUS rigid registration learning and optimization methods, for which we observe substantial and
significant performance decreases in four metrics. These results highlight the benefits of the changes of MUReg over the baselines, namely the new BBVRE loss and the final ICP refinement step. Although not novel, regressing rotations from masks enables us to avoid the MRI-TRUS domain gap, which hinders the results of deep learning baselines and pulls the optimization methods towards regions of high contrasts (e.g., the bladder boundary, see Figure 3).

**MUReg ablation study** We now perform ablations to assess the impact of the three steps of our pipeline. Interestingly, removing the ICP refinement only mildly hinders performance (0.13mm gap in SRE) and still considerably outperforms all baselines (which do not use ICP) by at least 0.49mm in SRE. Meanwhile, running ICP by itself reveals that it is highly sensitive to large initial misalignments, shown for instance by its large rotation error of 23.0°. This result further highlights the quality of the *T* estimates provided by the first two steps of MUReg, which provide a sufficiently close initialization for the final ICP step to yield state-of-the-art results in the full pipeline. Finally, we conduct an ablation on the first step, where moving the translation initial estimation to the regressor (which now predicts translation and rotation) leads to a slight drop in performance, where we observe differences with the full MUReg of 1.17mm and 1.13mm in translation error and BBVRE, respectively.

** Regressor ablation study ** Finally, we perform ablations on MUReg without ICP in order to study the impact of our design choices with respect to the baselines (since these do not use ICP). More precisely, we focus here on the regressor *f*<sub>*r*</sub> (as opposed to the segmentation networks) since all baselines already use the same center of mass initialization as MUReg. First, we see that the BBVRE loss leads to slight improvements over Dice, MSE, and geodesic losses. Then, replacing the input masks with masked images is very detrimental, which partly explains the lower scores of the learning baselines. Finally, further architecture ablations show that the attention block and the dual-stream encoder have a significant impact on performances.

4 Conclusion

We have presented MUReg, an automated pipeline for 3D MRI-TRUS rigid reg-
istration, consisting of three steps: segmentation-based translation estimation,
an attention-CNN trained with a new BBVRE loss for rotation regression, and
ICP refinement. This pipeline has been shown to significantly outperform in four
different metrics state-of-the-art optimization and learning strategies specifically
developed for this problem. Future work will first evaluate MUReg on datasets
acquired at different hospitals to demonstrate its generalizability. After having
addressed large rigid misalignments in MRI-TRUS registration, we will extend
MUReg to non-linear transforms to account for soft tissue deformation in the
prostate region. By improving the state-of-the-art in MRI-TRUS rigid registra-
tion, this work is a new advancement towards accurate image-guided biopsy.
**Acknowledgments.** This work has been supported by the French government, through the 3IA Cote d'Azur Investments in the project managed by the National Research Agency (ANR) with the reference number ANR-23-IACL-0001.

**Disclosure of Interests.** The authors have no competing interests.

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