Add BoundaryLoss

[txmed82]

Added BoundaryLoss. - Handles 2D/3D seg - Supports sigmoid, softmax, other_act, include_background, batch - Does empty masks without producing arbitrary distance ramps - Added tests for shape handling, reductions, gradient flow, single channel warnings, degenerative mask & channel free targets Verified with tests including run tests.sh

Closes #8884

[coderabbitai]

📝 Walkthrough

Walkthrough

This PR introduces BoundaryLoss, a new loss function for boundary-aware segmentation. It computes a signed distance map for each target class using scipy's distance_transform_edt, weights predicted probabilities by these distance maps to emphasize boundary regions, and normalizes by pixel count. The implementation supports configurable activation (sigmoid/softmax/custom), one-hot target conversion, background exclusion, and standard reduction modes. Full package integration includes init re-export and documentation entry. Tests cover parameterized configurations, shape validation, edge cases, error handling, and gradient propagation.

Estimated code review effort

🎯 3 (Moderate) | ⏱️ ~22 minutes

🚥 Pre-merge checks | ✅ 5

✅ Passed checks (5 passed)

Check name	Status	Explanation
Title check	✅ Passed	Title clearly and concisely summarizes the main change: adding a new BoundaryLoss class to the codebase.
Description check	✅ Passed	Description covers key implementation details and references the linked issue, but doesn't fully follow the template structure with explicit checkbox markings for all required sections.
Linked Issues check	✅ Passed	PR successfully implements all core objectives from #8884: BoundaryLoss class with 2D/3D support, activation options, distance-map weighting, standard reduction parameter, and comprehensive tests.
Out of Scope Changes check	✅ Passed	All changes directly support BoundaryLoss implementation: new loss class, module exports, documentation, and tests. No unrelated modifications detected.
Docstring Coverage	✅ Passed	Docstring coverage is 95.00% which is sufficient. The required threshold is 80.00%.

_{✏️ Tip: You can configure your own custom pre-merge checks in the settings.}

✨ Finishing Touches

🧪 Generate unit tests (beta)

• Create PR with unit tests

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[coderabbitai]

Actionable comments posted: 1

🤖 Prompt for all review comments with AI agents

Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.

Inline comments:
In `@tests/losses/test_boundary_loss.py`:
- Around line 151-154: Add a Google-style docstring to the helper function
_describe_test_case that documents the parameters and return value: describe the
arguments (test_func, test_number, params) and that params.args unpacks to
(input_param, input_data, _), explain the expected structure of input_data (a
dict with 'input' Tensor including shape and device), and state that the
function returns a formatted string describing the params, shape, and device;
place the docstring immediately under the def line in Google style with Args:
and Returns: sections.

🪄 Autofix (Beta)

Fix all unresolved CodeRabbit comments on this PR:

• Push a commit to this branch (recommended)
• Create a new PR with the fixes

---

ℹ️ Review info

⚙️ Run configuration

Configuration used: Path: .coderabbit.yaml

Review profile: CHILL

Plan: Pro

Run ID: 8cc5f9a0-8eb4-430a-bb9e-4ca7e4e8938d

📥 Commits

Reviewing files that changed from the base of the PR and between eccefc5 and 9c26ae6.

📒 Files selected for processing (4)

• docs/source/losses.rst
• monai/losses/__init__.py
• monai/losses/boundary_loss.py
• tests/losses/test_boundary_loss.py

[coderabbitai]

📐 Maintainability & Code Quality | 🟡 Minor | ⚡ Quick win

Add a Google-style docstring to _describe_test_case.

This helper definition is missing a docstring describing parameters and return value.

Proposed fix

 def _describe_test_case(test_func, test_number, params):
+    """Build a readable label for a parameterized test case.
+
+    Args:
+        test_func: The parameterized test function.
+        test_number: The generated case index.
+        params: Parameter bundle containing test args.
+
+    Returns:
+        A string describing params, tensor shape, and device.
+    """
     input_param, input_data, _ = params.args
     return f"params:{input_param}, shape:{input_data['input'].shape}, device:{input_data['input'].device}"

As per coding guidelines, "Docstrings should be present for all definition which describe each variable, return value, and raised exception in the appropriate section of the Google-style of docstrings."

📝 Committable suggestion

‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change

	def _describe_test_case(test_func, test_number, params):
	input_param, input_data, _ = params.args
	return f"params:{input_param}, shape:{input_data['input'].shape}, device:{input_data['input'].device}"
	def _describe_test_case(test_func, test_number, params):
	"""Build a readable label for a parameterized test case.
	Args:
	test_func: The parameterized test function.
	test_number: The generated case index.
	params: Parameter bundle containing test args.
	Returns:
	A string describing params, tensor shape, and device.
	"""
	input_param, input_data, _ = params.args
	return f"params:{input_param}, shape:{input_data['input'].shape}, device:{input_data['input'].device}"

🤖 Prompt for AI Agents

Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.

In `@tests/losses/test_boundary_loss.py` around lines 151 - 154, Add a
Google-style docstring to the helper function _describe_test_case that documents
the parameters and return value: describe the arguments (test_func, test_number,
params) and that params.args unpacks to (input_param, input_data, _), explain
the expected structure of input_data (a dict with 'input' Tensor including shape
and device), and state that the function returns a formatted string describing
the params, shape, and device; place the docstring immediately under the def
line in Google style with Args: and Returns: sections.

Source: Coding guidelines